diff --git a/.travis.yml b/.travis.yml index a8f8f34..8e7f756 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,6 +2,7 @@ language: python python: + # - 3.8 - 3.7 - 3.6 diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..c102840 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,35 @@ +# YAML 1.2 +--- +cff-version: "1.1.0" +message: "If you use this software, please cite it using these metadata." +title: CoPro +version: "v0.0.6b-joss" +authors: + - + affiliation: "Utrecht University" + family-names: Hoch + given-names: Jannis + orcid: "https://orcid.org/0000-0003-3570-6436" + - + affiliation: "PBL Netherlands Environmental Assessment Agency" + family-names: "de Bruine" + given-names: Sophie + orcid: "https://orcid.org/0000-0003-3429-349X" + - + affiliation: "Utrecht University" + family-names: Wanders + given-names: Niko + orcid: "https://orcid.org/0000-0002-7102-5454" +date-released: 2020-11-17 +doi: "10.5281/zenodo.4264684" +license: MIT +repository-code: "https://github.com/JannisHoch/copro" +keywords: + - conflict + - "machine learning" + - "climate change" + - water + - scenarios + - projections + - "climate security" +... \ No newline at end of file diff --git a/README.rst b/README.rst index c50bbf0..8d1e23b 100644 --- a/README.rst +++ b/README.rst @@ -7,8 +7,8 @@ CoPro Welcome to CoPro, a machine-learning tool for conflict risk projections based on climate, environmental, and societal drivers. -.. image:: https://travis-ci.com/JannisHoch/conflict_model.svg?branch=dev - :target: https://travis-ci.com/JannisHoch/conflict_model +.. image:: https://travis-ci.com/JannisHoch/copro.svg?branch=dev + :target: https://travis-ci.com/JannisHoch/copro .. image:: https://img.shields.io/badge/License-MIT-blue.svg :target: https://github.com/JannisHoch/copro/blob/dev/LICENSE @@ -17,7 +17,7 @@ Welcome to CoPro, a machine-learning tool for conflict risk projections based on :target: https://copro.readthedocs.io/en/latest/?badge=latest .. image:: https://img.shields.io/github/v/release/JannisHoch/copro - :target: https://github.com/JannisHoch/copro/releases/tag/v0.0.5-pre + :target: https://github.com/JannisHoch/copro/releases/tag/v0.0.6 .. image:: https://zenodo.org/badge/254407279.svg :target: https://zenodo.org/badge/latestdoi/254407279 @@ -67,12 +67,16 @@ Runner script To run the model from command line, a command line script is provided. All data and settings are retrieved from the settings-file which needs to be provided as inline argument. +There are two settings-files, one for evaluating the model for the reference situation, and another one for additionally making projections. +To make a projection, both files need to be specified with the latter requiring the -proj flag. + .. code-block:: console $ cd path/to/copro/scripts $ python runner.py ../example/example_settings.cfg + $ python runner.py ../example/example_settings.cfg -proj ../example/example_settings_proj.cfg -By default, output is stored to the output directory specified in the settings-file. +By default, output is stored to the output directory specified in the specific settings-file. Documentation --------------- diff --git a/conflict_model/plots.py b/conflict_model/plots.py deleted file mode 100644 index 445e88f..0000000 --- a/conflict_model/plots.py +++ /dev/null @@ -1,234 +0,0 @@ -import matplotlib.pyplot as plt -import geopandas as gpd -import seaborn as sbs -import numpy as np -import os, sys -from sklearn import metrics -from conflict_model import evaluation - -def plot_active_polys(conflict_gdf, polygon_gdf, config, out_dir, **kwargs): - """Creates a (1,2)-subplot showing a) selected conflicts and all polygons, and b) selected conflicts and selected polygons. - - Args: - conflict_gdf (geo-dataframe): geo-dataframe containing the selected conflicts. - extent_gdf (geo-dataframe): geo-dataframe containing all polygons. - polygon_gdf (geo-dataframe): geo-dataframe containing the selected polygons. - config (ConfigParser-object): object containing the parsed configuration-settings of the model. - out_dir (str): path to output folder - """ - - fig, ax = plt.subplots(1, 1, **kwargs) - fig.suptitle('conflict distribution; # conflicts {}; threshold casualties {}; type of violence {}'.format(len(conflict_gdf), config.get('conflict', 'min_nr_casualties'), config.get('conflict', 'type_of_violence'))) - - conflict_gdf.plot(ax=ax, c='r', column='best', cmap='magma', - vmin=int(config.get('conflict', 'min_nr_casualties')), vmax=conflict_gdf.best.mean(), - legend=True, - legend_kwds={'label': "# casualties",}) - polygon_gdf.boundary.plot(ax=ax) - - plt.savefig(os.path.join(out_dir, 'selected_conflicts_and_polygons.png'), dpi=300) - - return - -def plot_metrics_distribution(out_dict, out_dir, **kwargs): - """Plots the value distribution of a range of evaluation metrics based on all model simulations. - - Args: - out_dict (dict): dictionary containing metrics score for various metrics and all simulation. - out_dir (str): path to output folder. - """ - - fig, ax = plt.subplots(1, 1, **kwargs) - - sbs.distplot(out_dict['Accuracy'], ax=ax, color="k", label='Accuracy') - sbs.distplot(out_dict['Precision'], ax=ax, color="r", label='Precision') - sbs.distplot(out_dict['Recall'], ax=ax, color="b", label='Recall') - plt.legend() - - plt.savefig(os.path.join(out_dir, 'metrics_distribution.png'), dpi=300) - - return - -def plot_nr_and_dist_pred(df, gdf, polygon_gdf, out_dir, **kwargs): - """Plots the number of number of predictions made per unique polygon, and the overall value distribution. - - Args: - df (dataframe): containing model evaluation per unique polygon. - gdf (geo-dataframe): containing model evaluation per unique polygon. - polygon_gdf (geo-dataframe): geo-dataframe containing the selected polygons. - out_dir (str): path to output folder. - suffix (str, optional): suffix that can be used to discriminate between different analyses. Defaults to ''. - """ - - fig, (ax1, ax2) = plt.subplots(1, 2, **kwargs) - - gdf.plot(ax=ax1, column='ID_count', legend=True, cmap='cool') - polygon_gdf.boundary.plot(ax=ax1, color='0.5') - ax1.set_title('number of predictions made per polygon') - sbs.distplot(df.ID_count.values, ax=ax2) - ax2.set_title('distribution of predictions') - - plt.savefig(os.path.join(out_dir, 'analyis_predictions.png'), dpi=300) - - return - -def plot_predictiveness(gdf, polygon_gdf, out_dir): - """Creates (1,3)-subplot showing per polygon the chance of correct prediction, the number of conflicts, and the chance of correct conflict prediction. - - Args: - gdf (geo-dataframe): containing model evaluation per unique polygon. - polygon_gdf (geo-dataframe): geo-dataframe containing the selected polygons. - out_dir (str): path to output folder. - suffix (str, optional): suffix that can be used to discriminate between different analyses. Defaults to ''. - """ - - fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10)) - gdf.plot(ax=ax1, column='chance_correct_pred', legend=True, - legend_kwds={'label': "chance correct prediction", 'orientation': "horizontal"}) - polygon_gdf.boundary.plot(ax=ax1, color='0.5') - gdf.plot(ax=ax2, column='nr_test_confl', legend=True, cmap='Reds', - legend_kwds={'label': "nr of conflicts per polygon", 'orientation': "horizontal"}) - polygon_gdf.boundary.plot(ax=ax2, color='0.5') - gdf.plot(ax=ax3, column='chance_correct_confl_pred', legend=True, cmap='Blues', - legend_kwds={'label': "chance correct conflict prediction", 'orientation': "horizontal"}) - polygon_gdf.boundary.plot(ax=ax3, color='0.5') - - plt.savefig(os.path.join(out_dir, 'model_predictivness.png'), dpi=300) - - return - -def plot_correlation_matrix(df, out_dir): - """Plots the correlation matrix of a dataframe. - - Args: - df (dataframe): dataframe containing columns to be correlated. - out_dir (str): path to output folder - """ - - df_corr = evaluation.correlation_matrix(df) - - fig, (ax1) = plt.subplots(1, 1, figsize=(20, 10)) - sbs.heatmap(df_corr, cmap='YlGnBu', annot=True, cbar=False, ax=ax1) - plt.savefig(os.path.join(out_dir, 'correlation_matrix.png'), dpi=300) - -def plot_categories(gdf, out_dir, category='sub', mode='median'): - """Plots the categorization of polygons based on chance of correct prediction and number of conflicts. - - Main categories are: - * H: chance of correct prediction higher than treshold; - * L: chance of correct prediction lower than treshold. - - Sub-categories are: - * HH: high chance of correct prediction with high number of conflicts; - * HL: high chance of correct prediction with low number of conflicts; - * LH: low chance of correct prediction with high number of conflicts; - * LL: low chance of correct prediction with low number of conflicts. - - Args: - gdf (geo-dataframe): containing model evaluation per unique polygon. - out_dir (str): path to output folder - mode (str, optional): Statistical mode used to determine categorization threshold. Defaults to 'median'. - """ - - gdf = evaluation.categorize_polys(gdf, category, mode) - - fig, ax = plt.subplots(1, 1, figsize=(20, 10)) - gdf.plot(column='category', categorical=True, legend=True, ax=ax, cmap='copper') - - plt.savefig(os.path.join(out_dir, 'polygon_categorization.png'), dpi=300) - -def plot_ROC_curve_n_times(ax, clf, X_test, y_test, tprs, aucs, mean_fpr, **kwargs): - """Plots the ROC-curve per model simulation to a pre-initiated matplotlib-instance. - - Args: - ax (axis): axis of pre-initaited matplotlib-instance - clf (classifier): sklearn-classifier used in the simulation. - X_test (array): array containing test-sample variable values. - y_test (list): list containing test-sample conflict data. - tprs (list): list with false positive rates. - aucs (list): list with area-under-curve values. - mean_fpr (array): array with mean false positive rate. - - Returns: - list: lists with true positive rates and area-under-curve values per plot. - """ - - print(len(X_test), len(y_test)) - viz = metrics.plot_roc_curve(clf, X_test, y_test, ax=ax, - alpha=0.15, color='b', lw=1, label=None, **kwargs) - - interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr) - interp_tpr[0] = 0.0 - tprs.append(interp_tpr) - aucs.append(viz.roc_auc) - - return tprs, aucs - -def plot_ROC_curve_n_mean(ax, tprs, aucs, mean_fpr, **kwargs): - """Plots the mean ROC-curve to a pre-initiated matplotlib-instance. - - Args: - ax (axis): axis of pre-initaited matplotlib-instance - tprs (list): list with false positive rates. - aucs (list): list with area-under-curve values. - mean_fpr (array): array with mean false positive rate. - """ - - mean_tpr = np.mean(tprs, axis=0) - mean_tpr[-1] = 1.0 - mean_auc = metrics.auc(mean_fpr, mean_tpr) - std_auc = np.std(aucs) - ax.plot(mean_fpr, mean_tpr, color='r', - label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc), - lw=2, alpha=.8, **kwargs) - - std_tpr = np.std(tprs, axis=0) - tprs_upper = np.minimum(mean_tpr + std_tpr, 1) - tprs_lower = np.maximum(mean_tpr - std_tpr, 0) - ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2, label=None, **kwargs) - - ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], **kwargs) - - ax.legend(loc="lower right") - - return - -def plot_kFold_polygon_analysis(y_df, global_df, out_dir, **kwargs): - """Determines the mean and standard deviation of correct chance of prediction (CCP) per polygon. - - Args: - y_df (dataframe): output dataframe containing results of all simulations. - global_df (dataframe): global look-up dataframe to associate unique identifier with geometry. - out_dir (str): path to output folder. - """ - - gdf = evaluation.calc_kFold_polygon_analysis(y_df, global_df, out_dir) - - fig, (ax1, ax2) = plt.subplots(1, 2, **kwargs) - gdf.plot(column='mean_CCP', ax=ax1, legend=True) - ax1.set_title('MEAN') - gdf.plot(column='std_CCP', ax=ax2, legend=True) - ax2.set_title('STD') - - plt.savefig(os.path.join(out_dir, 'mean_and_std_CCP.png'), dpi=300) - - return - -def plot_confusion_matrix(clf, out_X_df, out_y_df, out_dir): - """Plots the confusion matrix based on all data points. - - Args: - clf (classifier): classifier used. - out_X_df (dataframe): dataframe with all observations. - out_y_df (dataframe): dataframe with all predictions. - out_dir (str): path to output folder. - """ - - fig, ax = plt.subplots(1, 1, figsize=(20, 10)) - - metrics.plot_confusion_matrix(clf, out_X_df.to_numpy(), out_y_df.y_test.to_list(), ax=ax) - - plt.savefig(os.path.join(out_dir, 'confusion_matrix.png'), dpi=300) - - return - diff --git a/copro/__init__.py b/copro/__init__.py index b7213a7..cb4e012 100644 --- a/copro/__init__.py +++ b/copro/__init__.py @@ -11,6 +11,6 @@ from . import models from . import plots -__author__ = """Jannis M. Hoch, Niko Wanders, Sophie de Bruin""" +__author__ = """Jannis M. Hoch, Sophie de Bruin, Niko Wanders""" __email__ = 'j.m.hoch@uu.nl' -__version__ = '0.0.5' +__version__ = '0.0.6' diff --git a/copro/conflict.py b/copro/conflict.py index 2379930..cc1b9db 100644 --- a/copro/conflict.py +++ b/copro/conflict.py @@ -1,7 +1,6 @@ import geopandas as gpd import pandas as pd import numpy as np -import matplotlib.pyplot as plt import os, sys def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year): @@ -10,7 +9,7 @@ def conflict_in_year_bool(conflict_gdf, extent_gdf, config, sim_year): Args: conflict_gdf (geodataframe): geo-dataframe containing georeferenced information of conflict (tested with PRIO/UCDP data) extent_gdf (geodataframe): geo-dataframe containing one or more polygons with geometry information for which values are extracted - config (config): parsed configuration settings of run + config (ConfigParser-object): object containing the parsed configuration-settings of the model. sim_year (int): year for which data is extracted Raises: @@ -80,11 +79,12 @@ def get_poly_ID(extent_gdf): return list_ID -def get_poly_geometry(extent_gdf): +def get_poly_geometry(extent_gdf, config): """Extracts geometry information for each polygon from geodataframe and saves to list. The geometry column in geodataframe must be named 'geometry'. Args: extent_gdf (geo-dataframe): geo-dataframe containing one or more polygons with geometry information. + config (ConfigParser-object): object containing the parsed configuration-settings of the model. Raises: AssertionError: error raised if length of output list does not match length of input geo-dataframe. @@ -93,7 +93,7 @@ def get_poly_geometry(extent_gdf): list: list containing the geometry information extracted from geo-dataframe for each polygon used in the model. """ - print('listing the geometry of all geographical units') + if config.getboolean('general', 'verbose'): print('DEBUG: getting the geometry of all geographical units') # initiatie empty list list_geometry = [] diff --git a/copro/data.py b/copro/data.py index 969ab89..133b9fd 100644 --- a/copro/data.py +++ b/copro/data.py @@ -8,19 +8,19 @@ def initiate_XY_data(config): """Initiates an empty dictionary to contain the XY-data for each polygon. By default, the first column is for the polygon ID, the second for polygon geometry, and the last for binary conflict data (i.e. the Y-data). - Every column in between corresponds to the variables providing in the cfg-file (i.e. the X-data). + Every column in between corresponds to the variables provided in the cfg-file (i.e. the X-data). Args: config (ConfigParser-object): object containing the parsed configuration-settings of the model. Returns: - dict: emtpy dictionary containing the variable values (X) and binary conflict data (Y) plus meta-data. + dict: emtpy dictionary to be filled, containing keys for each variable (X), binary conflict data (Y) plus meta-data. """ XY = {} XY['poly_ID'] = pd.Series() XY['poly_geometry'] = pd.Series() - for key in config.items('env_vars'): + for key in config.items('data'): XY[str(key[0])] = pd.Series(dtype=float) XY['conflict'] = pd.Series(dtype=int) @@ -28,6 +28,28 @@ def initiate_XY_data(config): return XY +def initiate_X_data(config): + """Initiates an empty dictionary to contain the X-data for each polygon. + By default, the first column is for the polygon ID and the second for polygon geometry. + All remaining columns correspond to the variables provided in the cfg-file (i.e. the X-data). + + Args: + config (ConfigParser-object): object containing the parsed configuration-settings of the model. + + Returns: + dict: emtpy dictionary to be filled, containing keys for each variable (X) plus meta-data. + """ + + X = {} + X['poly_ID'] = pd.Series() + X['poly_geometry'] = pd.Series() + for key in config.items('data'): + X[str(key[0])] = pd.Series(dtype=float) + + if config.getboolean('general', 'verbose'): print('{}'.format(X) + os.linesep) + + return X + def fill_XY(XY, config, conflict_gdf, polygon_gdf): """Fills the XY-dictionary with data for each variable and conflict for each polygon for each simulation year. The number of rows should therefore equal to number simulation years times number of polygons. @@ -46,12 +68,13 @@ def fill_XY(XY, config, conflict_gdf, polygon_gdf): array: filled array containing the variable values (X) and binary conflict data (Y) plus meta-data. """ - if config.getboolean('general', 'verbose'): print('reading data for period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end')) + os.linesep) + print('INFO: reading data for period from', str(config.getint('settings', 'y_start')), 'to', str(config.getint('settings', 'y_end'))) # go through all simulation years as specified in config-file - for sim_year in np.arange(config.getint('settings', 'y_start'), config.getint('settings', 'y_end'), 1): + model_period = np.arange(config.getint('settings', 'y_start'), config.getint('settings', 'y_end') + 1, 1) + for sim_year in model_period: - if config.getboolean('general', 'verbose'): print(os.linesep + 'entering year {}'.format(sim_year) + os.linesep) + print('INFO: entering year {}'.format(sim_year)) # go through all keys in dictionary for key, value in XY.items(): @@ -73,13 +96,13 @@ def fill_XY(XY, config, conflict_gdf, polygon_gdf): elif key == 'poly_geometry': data_series = value - data_list = conflict.get_poly_geometry(polygon_gdf) + data_list = conflict.get_poly_geometry(polygon_gdf, config) data_series = data_series.append(pd.Series(data_list), ignore_index=True) XY[key] = data_series else: - - nc_ds = xr.open_dataset(os.path.join(config.get('general', 'input_dir'), config.get('env_vars', key))) + + nc_ds = xr.open_dataset(os.path.join(config.get('general', 'input_dir'), config.get('data', key))) if (np.dtype(nc_ds.time) == np.float32) or (np.dtype(nc_ds.time) == np.float64): data_series = value @@ -94,9 +117,9 @@ def fill_XY(XY, config, conflict_gdf, polygon_gdf): XY[key] = data_series else: - raise Warning('this nc-file does have a different dtype for the time variable than currently supported: {}'.format(nc_fo)) + raise Warning('WARNING: this nc-file does have a different dtype for the time variable than currently supported: {}'.format(nc_fo)) - if config.getboolean('general', 'verbose'): print('...reading data DONE' + os.linesep) + print('INFO: all data read') return pd.DataFrame.from_dict(XY).to_numpy() @@ -105,17 +128,18 @@ def split_XY_data(XY, config): Thereby, the X-array also contains the information about unique identifier and polygon geometry. Args: - XY (array): array containing variable values and conflict data + XY (array): array containing variable values and conflict data. + config (ConfigParser-object): object containing the parsed configuration-settings of the model. Returns: arrays: two separate arrays, the X-array and Y-array """ XY = pd.DataFrame(XY) - if config.getboolean('general', 'verbose'): print('number of data points including missing values:', len(XY)) + if config.getboolean('general', 'verbose'): print('DEBUG: number of data points including missing values:', len(XY)) XY = XY.dropna() - if config.getboolean('general', 'verbose'): print('number of data points excluding missing values:', len(XY)) + if config.getboolean('general', 'verbose'): print('DEBUG: number of data points excluding missing values:', len(XY)) XY = XY.to_numpy() X = XY[:, :-1] # since conflict is the last column, we know that all previous columns must be variable values @@ -124,6 +148,6 @@ def split_XY_data(XY, config): if config.getboolean('general', 'verbose'): fraction_Y_1 = 100*len(np.where(Y != 0)[0])/len(Y) - print('from this, {0} points are equal to 1, i.e. represent conflict occurence. This is a fraction of {1} percent.'.format(len(np.where(Y != 0)[0]), round(fraction_Y_1, 2))) + print('DEBUG: a fraction of {} percent in the data corresponds to conflicts.'.format(round(fraction_Y_1, 2))) return X, Y \ No newline at end of file diff --git a/copro/evaluation.py b/copro/evaluation.py index 3dc101a..e954787 100644 --- a/copro/evaluation.py +++ b/copro/evaluation.py @@ -1,10 +1,9 @@ import os, sys -import warnings +import click from sklearn import metrics import pandas as pd import geopandas as gpd import numpy as np -import matplotlib.pyplot as plt def init_out_dict(): """Initiates the main model evaluatoin dictionary for a range of model metric scores. @@ -25,6 +24,7 @@ def init_out_dict(): def evaluate_prediction(y_test, y_pred, y_prob, X_test, clf, config): """Computes a range of model evaluation metrics and appends the resulting scores to a dictionary. This is done for each model execution separately. + Output will be stored to stderr if possible. Args: y_test (list): list containing test-sample conflict data. @@ -39,17 +39,13 @@ def evaluate_prediction(y_test, y_pred, y_prob, X_test, clf, config): """ if config.getboolean('general', 'verbose'): - print("Accuracy: {0:0.3f}".format(metrics.accuracy_score(y_test, y_pred))) - print("Precision: {0:0.3f}".format(metrics.precision_score(y_test, y_pred))) - print("Recall: {0:0.3f}".format(metrics.recall_score(y_test, y_pred))) - print('F1 score: {0:0.3f}'.format(metrics.f1_score(y_test, y_pred))) - print('Brier loss score: {0:0.3f}'.format(metrics.brier_score_loss(y_test, y_prob[:, 1]))) - print('Cohen-Kappa score: {0:0.3f}'.format(metrics.cohen_kappa_score(y_test, y_pred))) - print('ROC AUC score {0:0.3f}'.format(metrics.roc_auc_score(y_test, y_prob[:, 1]))) - print('') - - print(metrics.classification_report(y_test, y_pred)) - print('') + click.echo("... Accuracy: {0:0.3f}".format(metrics.accuracy_score(y_test, y_pred)), err=True) + click.echo("... Precision: {0:0.3f}".format(metrics.precision_score(y_test, y_pred)), err=True) + click.echo("... Recall: {0:0.3f}".format(metrics.recall_score(y_test, y_pred)), err=True) + click.echo('... F1 score: {0:0.3f}'.format(metrics.f1_score(y_test, y_pred)), err=True) + click.echo('... Brier loss score: {0:0.3f}'.format(metrics.brier_score_loss(y_test, y_prob[:, 1])), err=True) + click.echo('... Cohen-Kappa score: {0:0.3f}'.format(metrics.cohen_kappa_score(y_test, y_pred)), err=True) + click.echo('... ROC AUC score {0:0.3f}'.format(metrics.roc_auc_score(y_test, y_prob[:, 1])), err=True) eval_dict = {'Accuracy': metrics.accuracy_score(y_test, y_pred), 'Precision': metrics.precision_score(y_test, y_pred), @@ -105,7 +101,7 @@ def fill_out_df(out_df, y_df): return out_df -def polygon_model_accuracy(df, global_df, out_dir): +def polygon_model_accuracy(df, global_df, out_dir, make_proj=False): """Determines a range of model accuracy values for each polygon. Reduces dataframe with results from each simulation to values per unique polygon identifier. Determines the total number of predictions made per polygon as well as fraction of correct predictions made for overall and conflict-only data. @@ -113,14 +109,15 @@ def polygon_model_accuracy(df, global_df, out_dir): Args: df (dataframe): output dataframe containing results of all simulations. global_df (dataframe): global look-up dataframe to associate unique identifier with geometry. - out_dir (str): path to output folder. If None, output is not saved. + out_dir (str): path to output folder. If 'None', no output is stored. + make_proj (bool, optional): whether or not this function is used to make a projection. If False, a couple of calculations are skipped. Defaults to 'False'. Returns: (geo-)dataframe: dataframe and geo-dataframe with data per polygon. """ #- create a dataframe containing the number of occurence per ID - ID_count = df.ID.value_counts().to_frame().rename(columns={'ID':'ID_count'}) + ID_count = df.ID.value_counts().to_frame().rename(columns={'ID':'nr_predictions'}) #- add column containing the IDs ID_count['ID'] = ID_count.index.values #- set index with index named ID now @@ -128,23 +125,25 @@ def polygon_model_accuracy(df, global_df, out_dir): #- remove column ID ID_count = ID_count.drop('ID', axis=1) + df_count = pd.DataFrame() + #- per polygon ID, compute sum of overall correct predictions and rename column name - hit_count = df.correct_pred.groupby(df.ID).sum().to_frame() + if not make_proj: df_count['nr_correct_predictions'] = df.correct_pred.groupby(df.ID).sum() #- per polygon ID, compute sum of all conflict data points and add to dataframe - hit_count['nr_test_confl'] = df.y_test.groupby(df.ID).sum() + if not make_proj: df_count['nr_observed_conflicts'] = df.y_test.groupby(df.ID).sum() #- per polygon ID, compute sum of all conflict data points and add to dataframe - hit_count['nr_pred_confl'] = df.y_pred.groupby(df.ID).sum() + df_count['nr_predicted_conflicts'] = df.y_pred.groupby(df.ID).sum() #- merge the two dataframes with ID as key - df_temp = pd.merge(ID_count, hit_count, on='ID') + df_temp = pd.merge(ID_count, df_count, on='ID') #- compute average correct prediction rate by dividing sum of correct predictions with number of all predicionts - df_temp['chance_correct_pred'] = df_temp.correct_pred / df_temp.ID_count + if not make_proj: df_temp['fraction_correct_predictions'] = df_temp.nr_correct_predictions / df_temp.nr_predictions #- compute average correct prediction rate by dividing sum of correct predictions with number of all predicionts - df_temp['chance_correct_confl_pred'] = df_temp.nr_pred_confl / df_temp.ID_count + df_temp['fraction_correct_conflict_predictions'] = df_temp.nr_predicted_conflicts / df_temp.nr_predictions #- merge with global dataframe containing IDs and geometry, and keep only those polygons occuring in test sample df_hit = pd.merge(df_temp, global_df, on='ID', how='left') @@ -152,8 +151,8 @@ def polygon_model_accuracy(df, global_df, out_dir): #- convert to geodataframe gdf_hit = gpd.GeoDataFrame(df_hit, geometry=df_hit.geometry) - if out_dir != None: - gdf_hit.to_file(os.path.join(out_dir, 'all_stats.shp'), crs='EPSG:4326') + if (out_dir != None) and isinstance(out_dir, str): + gdf_hit.to_file(os.path.join(out_dir, 'output_per_polygon.shp'), crs='EPSG:4326') return df_hit, gdf_hit @@ -170,6 +169,26 @@ def init_out_ROC_curve(): return tprs, aucs, mean_fpr +def save_out_ROC_curve(tprs, aucs, out_dir): + """Saves data needed to plot mean ROC and standard deviation to csv-files. + They can be loaded again with pandas in a post-processing step. + + Args: + tprs (list): list with false positive rates. + aucs (list): list with area-under-curve values. + out_dir (str): path to output folder. If 'None', no output is stored. + """ + + tprs = pd.DataFrame(tprs) + aucs = pd.DataFrame(aucs) + + tprs.to_csv(os.path.join(out_dir, 'ROC_data_tprs.csv'), index=False, header=False) + aucs.to_csv(os.path.join(out_dir, 'ROC_data_aucs.csv'), index=False, header=False) + + print('INFO: saving ROC data to {}'.format(os.path.join(out_dir, 'ROC_data.csv'))) + + return + def calc_correlation_matrix(df): """Computes the correlation matrix for a dataframe. @@ -213,29 +232,29 @@ def categorize_polys(gdf_hit, category='sub', mode='median'): """ if mode == 'median': - average_hit_median = gdf_hit.chance_correct_pred.median() - nr_confl_median = gdf_hit.nr_test_confl.median() + average_hit_median = gdf_hit.fraction_correct_predictions.median() + nr_confl_median = gdf_hit.nr_observed_conflicts.median() elif mode == 'mean': - average_hit_median = gdf_hit.chance_correct_pred.mean() - nr_confl_median = gdf_hit.nr_test_confl.mean() + average_hit_median = gdf_hit.fraction_correct_predictions.mean() + nr_confl_median = gdf_hit.nr_observed_conflicts.mean() else: raise ValueError('specified mode not supported - use either median (default) or mean') gdf_hit['category'] = '' if category == 'main': - gdf_hit['category'].loc[gdf_hit.chance_correct_pred >= average_hit_median] = 'H' - gdf_hit['category'].loc[gdf_hit.chance_correct_pred < average_hit_median] = 'L' + gdf_hit['category'].loc[gdf_hit.fraction_correct_predictions >= average_hit_median] = 'H' + gdf_hit['category'].loc[gdf_hit.fraction_correct_predictions < average_hit_median] = 'L' if category == 'sub': - gdf_hit['category'].loc[(gdf_hit.chance_correct_pred >= average_hit_median) & - (gdf_hit.nr_test_confl >= nr_confl_median)] = 'HH' - gdf_hit['category'].loc[(gdf_hit.chance_correct_pred >= average_hit_median) & - (gdf_hit.nr_test_confl < nr_confl_median)] = 'HL' - gdf_hit['category'].loc[(gdf_hit.chance_correct_pred < average_hit_median) & - (gdf_hit.nr_test_confl >= nr_confl_median)] = 'LH' - gdf_hit['category'].loc[(gdf_hit.chance_correct_pred < average_hit_median) & - (gdf_hit.nr_test_confl < nr_confl_median)] = 'LL' + gdf_hit['category'].loc[(gdf_hit.fraction_correct_predictions >= average_hit_median) & + (gdf_hit.nr_observed_conflicts >= nr_confl_median)] = 'HH' + gdf_hit['category'].loc[(gdf_hit.fraction_correct_predictions >= average_hit_median) & + (gdf_hit.nr_observed_conflicts < nr_confl_median)] = 'HL' + gdf_hit['category'].loc[(gdf_hit.fraction_correct_predictions < average_hit_median) & + (gdf_hit.nr_observed_conflicts >= nr_confl_median)] = 'LH' + gdf_hit['category'].loc[(gdf_hit.fraction_correct_predictions < average_hit_median) & + (gdf_hit.nr_observed_conflicts < nr_confl_median)] = 'LL' return gdf_hit @@ -246,7 +265,7 @@ def calc_kFold_polygon_analysis(y_df, global_df, out_dir, k=10): Args: y_df (dataframe): output dataframe containing results of all simulations. global_df (dataframe): global look-up dataframe to associate unique identifier with geometry. - out_dir (str): path to output folder. If None, output is not saved. + out_dir (str): path to output folder. If 'None', no output is stored. k (int, optional): number of chunks in which y_df will be split. Defaults to 10. Returns: @@ -263,7 +282,7 @@ def calc_kFold_polygon_analysis(y_df, global_df, out_dir, k=10): df_hit, gdf_hit = polygon_model_accuracy(ks_i, global_df, out_dir=None) - temp_df = pd.DataFrame(data=pd.concat([df_hit.chance_correct_pred], axis=1)) + temp_df = pd.DataFrame(data=pd.concat([df_hit.fraction_correct_predictions], axis=1)) df = pd.concat([df, temp_df], axis=1) @@ -273,12 +292,12 @@ def calc_kFold_polygon_analysis(y_df, global_df, out_dir, k=10): df = pd.merge(df, global_df, on='ID') - df = df.drop(columns=['chance_correct_pred']) + df = df.drop(columns=['fraction_correct_predictions']) gdf = gpd.GeoDataFrame(df, geometry=df.geometry) - if out_dir != None: - gdf.to_file(os.path.join(out_dir, 'kFold_CCP_stats.shp'), crs='EPSG:4326') + if (out_dir != None) and isinstance(out_dir, str): + gdf.to_file(os.path.join(out_dir, 'output_kFoldAnalysis_per_polygon.shp'), crs='EPSG:4326') return gdf @@ -289,7 +308,7 @@ def get_feature_importance(clf, config, out_dir): Args: clf (classifier): sklearn-classifier used in the simulation. config (ConfigParser-object): object containing the parsed configuration-settings of the model. - out_dir (str): path to output folder. If None, output is not saved. + out_dir (str): path to output folder. If 'None', no output is stored. Returns: dataframe: dataframe containing feature importance. @@ -298,16 +317,16 @@ def get_feature_importance(clf, config, out_dir): if config.get('machine_learning', 'model') == 'RFClassifier': arr = clf.feature_importances_ else: - arr = np.zeros(len(config.items('env_vars'))) - warnings.warn('WARNING: feature importance not supported for this kind of ML model', UserWarning) + arr = np.zeros(len(config.items('data'))) + raise Warning('WARNING: feature importance not supported for this kind of ML model') dict_out = dict() - for key, x in zip(config.items('env_vars'), range(len(arr))): + for key, x in zip(config.items('data'), range(len(arr))): dict_out[key[0]] = arr[x] df = pd.DataFrame.from_dict(dict_out, orient='index', columns=['feature_importance']) - if out_dir != None: - df.to_csv(os.path.join(out_dir, 'feature_importance.csv')) + if (out_dir != None) and isinstance(out_dir, str): + df.to_csv(os.path.join(out_dir, 'feature_importances.csv')) return df \ No newline at end of file diff --git a/copro/machine_learning.py b/copro/machine_learning.py index 653808f..4b6d97b 100644 --- a/copro/machine_learning.py +++ b/copro/machine_learning.py @@ -1,9 +1,9 @@ import os +import pickle import pandas as pd import numpy as np -import matplotlib.pyplot as plt from sklearn import svm, neighbors, ensemble, preprocessing, model_selection, metrics -from copro import conflict +from copro import conflict, data def define_scaling(config): """Defines scaling method based on model configurations. @@ -29,7 +29,7 @@ def define_scaling(config): else: raise ValueError('no supported scaling-algorithm selected - choose between MinMaxScaler, StandardScaler, RobustScaler or QuantileTransformer') - if config.getboolean('general', 'verbose'): print('chosen scaling method is {}'.format(scaler)) + if config.getboolean('general', 'verbose'): print('DEBUG: chosen scaling method is {}'.format(scaler)) return scaler @@ -55,7 +55,7 @@ def define_model(config): else: raise ValueError('no supported ML model selected - choose between NuSVC, KNeighborsClassifier or RFClassifier') - if config.getboolean('general', 'verbose'): print('chosen ML model is {}'.format(clf)) + if config.getboolean('general', 'verbose'): print('DEBUG: chosen ML model is {}'.format(clf)) return clf @@ -81,14 +81,14 @@ def split_scale_train_test_split(X, Y, config, scaler): ##- separate arrays for geomety and variable values X_ID, X_geom, X_data = conflict.split_conflict_geom_data(X) - if config.getboolean('general', 'verbose'): print('fitting and transforming X' + os.linesep) + if config.getboolean('general', 'verbose'): print('DEBUG: fitting and transforming X') ##- scaling only the variable values X_ft = scaler.fit_transform(X_data) ##- combining geometry and scaled variable values X_cs = np.column_stack((X_ID, X_geom, X_ft)) - if config.getboolean('general', 'verbose'): print('splitting both X and Y in train and test data' + os.linesep) + if config.getboolean('general', 'verbose'): print('DEBUG: splitting both X and Y in train and test data') ##- splitting in train and test samples X_train, X_test, y_train, y_test = model_selection.train_test_split(X_cs, Y, @@ -105,7 +105,7 @@ def split_scale_train_test_split(X, Y, config, scaler): return X_train, X_test, y_train, y_test, X_train_geom, X_test_geom, X_train_ID, X_test_ID -def fit_predict(X_train, y_train, X_test, clf): +def fit_predict(X_train, y_train, X_test, clf, config, pickle_dump=True): """Fits the classifier based on training-data and makes predictions. Additionally, the prediction probability is determined. @@ -125,4 +125,38 @@ def fit_predict(X_train, y_train, X_test, clf): y_prob = clf.predict_proba(X_test) - return y_pred, y_prob \ No newline at end of file + return y_pred, y_prob + +def pickle_clf(scaler, clf, config): + """(Re)fits a classifier with all available data and pickles it. + Can then be used to make projections in conjuction with projected values. + + Args: + scaler (scaler): the specified scaling method instance. + clf (classifier): the specified model instance. + config (ConfigParser-object): object containing the parsed configuration-settings of the model. + + Returns: + classifier: classifier fitted with all available data. + """ + + print('INFO: fitting the classifier with all data from reference period') + + if config.get('pre_calc', 'XY') is '': + if config.getboolean('general', 'verbose'): print('DEBUG: loading XY data from {}'.format(os.path.abspath(os.path.join(config.get('general', 'output_dir'), 'XY.npy')))) + XY_fit = np.load(os.path.abspath(os.path.join(config.get('general', 'output_dir'), 'XY.npy')), allow_pickle=True) + else: + if config.getboolean('general', 'verbose'): print('DEBUG: loading XY data from {}'.format(os.path.abspath(config.get('pre_calc', 'XY')))) + XY_fit = np.load(os.path.abspath(os.path.join(config.get('general', 'output_dir'), config.get('pre_calc', 'XY'))), allow_pickle=True) + + X_fit, Y_fit = data.split_XY_data(XY_fit, config) + X_ID_fit, X_geom_fit, X_data_fit = conflict.split_conflict_geom_data(X_fit) + X_ft_fit = scaler.fit_transform(X_data_fit) + + clf.fit(X_ft_fit, Y_fit) + + print('INFO: dumping classifier to {}'.format(os.path.abspath(os.path.join(config.get('general', 'output_dir'), 'clf.pkl')))) + with open(os.path.abspath(os.path.join(config.get('general', 'output_dir'), 'clf.pkl')), 'wb') as f: + pickle.dump(clf, f) + + return clf \ No newline at end of file diff --git a/copro/models.py b/copro/models.py index 0f0f8c4..d39747f 100644 --- a/copro/models.py +++ b/copro/models.py @@ -1,7 +1,7 @@ -from copro import machine_learning, conflict, utils, evaluation +from copro import machine_learning, conflict, utils, evaluation, data import pandas as pd import numpy as np - +import pickle import os, sys def all_data(X, Y, config, scaler, clf, out_dir): @@ -20,17 +20,11 @@ def all_data(X, Y, config, scaler, clf, out_dir): datatrame: containing model output on polygon-basis. dict: dictionary containing evaluation metrics per simulation. """ - - if not config.getboolean('general', 'verbose'): - orig_stdout = sys.stdout - f = open(os.path.join(out_dir, 'out.txt'), 'w') - sys.stdout = f - - print('### USING ALL DATA ###' + os.linesep) + print('INFO: using all data') X_train, X_test, y_train, y_test, X_train_geom, X_test_geom, X_train_ID, X_test_ID = machine_learning.split_scale_train_test_split(X, Y, config, scaler) - y_pred, y_prob = machine_learning.fit_predict(X_train, y_train, X_test, clf) + y_pred, y_prob = machine_learning.fit_predict(X_train, y_train, X_test, clf, config) eval_dict = evaluation.evaluate_prediction(y_test, y_pred, y_prob, X_test, clf, config) @@ -38,10 +32,6 @@ def all_data(X, Y, config, scaler, clf, out_dir): X_df = pd.DataFrame(X_test) - if not config.getboolean('general', 'verbose'): - sys.stdout = orig_stdout - f.close() - return X_df, y_df, eval_dict def leave_one_out(X, Y, config, scaler, clf, out_dir): @@ -57,20 +47,18 @@ def leave_one_out(X, Y, config, scaler, clf, out_dir): scaler (scaler): the specified scaling method instance. clf (classifier): the specified model instance. out_dir (str): path to output folder. - """ - if not config.getboolean('general', 'verbose'): - orig_stdout = sys.stdout - f = open(os.path.join(out_dir, 'out.txt'), 'w') - sys.stdout = f + Raises: + DeprecationWarning: this function will most likely be deprecated due to lack of added value and applicability. + """ - print('### LEAVE ONE OUT MODEL ###' + os.linesep) + raise DeprecationWarning('WARNING: the leave-one-out model will be most likely be deprecated in near future') X_train, X_test, y_train, y_test, X_train_geom, X_test_geom, X_train_ID, X_test_ID = machine_learning.split_scale_train_test_split(X, Y, config, scaler) - for i, key in zip(range(X_train.shape[1]), config.items('env_vars')): + for i, key in zip(range(X_train.shape[1]), config.items('data')): - print('--- removing data for variable {} ---'.format(key[0]) + os.linesep) + print('INFO: removing data for variable {}'.format(key[0])) X_train_loo = np.delete(X_train, i, axis=1) X_test_loo = np.delete(X_test, i, axis=1) @@ -79,17 +67,13 @@ def leave_one_out(X, Y, config, scaler, clf, out_dir): if not os.path.isdir(sub_out_dir): os.makedirs(sub_out_dir) - y_pred, y_prob = machine_learning.fit_predict(X_train_loo, y_train, X_test_loo, clf) + y_pred, y_prob = machine_learning.fit_predict(X_train_loo, y_train, X_test_loo, clf, config) eval_dict = evaluation.evaluate_prediction(y_test, y_pred, y_prob, X_test_loo, clf, config) utils.save_to_csv(eval_dict, sub_out_dir, 'evaluation_metrics') - if not config.getboolean('general', 'verbose'): - sys.stdout = orig_stdout - f.close() - - sys.exit('With LEAVE-ONE-OUT model, execution stops here.') + sys.exit('INFO: leave-one-out model execution stops here.') def single_variables(X, Y, config, scaler, clf, out_dir): """Model workflow when the model is based on only one single variable. Output is limited to the metric scores. @@ -104,20 +88,18 @@ def single_variables(X, Y, config, scaler, clf, out_dir): scaler (scaler): the specified scaling method instance. clf (classifier): the specified model instance. out_dir (str): path to output folder. - """ - if not config.getboolean('general', 'verbose'): - orig_stdout = sys.stdout - f = open(os.path.join(out_dir, 'out.txt'), 'w') - sys.stdout = f + Raises: + DeprecationWarning: this function will most likely be deprecated due to lack of added value and applicability. + """ - print('### SINGLE VARIABLE MODEL ###' + os.linesep) + raise DeprecationWarning('WARNING: the single-variable model will be most likely be deprecated in near future') X_train, X_test, y_train, y_test, X_train_geom, X_test_geom, X_train_ID, X_test_ID = machine_learning.split_scale_train_test_split(X, Y, config, scaler) - for i, key in zip(range(X_train.shape[1]), config.items('env_vars')): + for i, key in zip(range(X_train.shape[1]), config.items('data')): - print('--- single variable model with variable {} ---'.format(key[0]) + os.linesep) + print('INFO: single-variable model with variable {}'.format(key[0])) X_train_svmod = X_train[:, i].reshape(-1, 1) X_test_svmod = X_test[:, i].reshape(-1, 1) @@ -126,17 +108,13 @@ def single_variables(X, Y, config, scaler, clf, out_dir): if not os.path.isdir(sub_out_dir): os.makedirs(sub_out_dir) - y_pred, y_prob = machine_learning.fit_predict(X_train_svmod, y_train, X_test_svmod, clf) + y_pred, y_prob = machine_learning.fit_predict(X_train_svmod, y_train, X_test_svmod, clf, config) eval_dict = evaluation.evaluate_prediction(y_test, y_pred, y_prob, X_test_svmod, clf, config) utils.save_to_csv(eval_dict, sub_out_dir, 'evaluation_metrics') - if not config.getboolean('general', 'verbose'): - sys.stdout = orig_stdout - f.close() - - sys.exit('With SINGLE VARIABLE model, execution stops here.') + sys.exit('INFO: single-variable model execution stops here.') def dubbelsteen(X, Y, config, scaler, clf, out_dir): """Model workflow when the relation between variables and conflict is based on randomness. @@ -155,29 +133,59 @@ def dubbelsteen(X, Y, config, scaler, clf, out_dir): dataframe: containing the test-data X-array values. datatrame: containing model output on polygon-basis. dict: dictionary containing evaluation metrics per simulation. - """ - - if not config.getboolean('general', 'verbose'): - orig_stdout = sys.stdout - f = open(os.path.join(out_dir, 'out.txt'), 'w') - sys.stdout = f + """ - print('### DUBBELSTEENMODEL ###' + os.linesep) + print('INFO: dubbelsteenmodel running') Y = utils.create_artificial_Y(Y) X_train, X_test, y_train, y_test, X_train_geom, X_test_geom, X_train_ID, X_test_ID = machine_learning.split_scale_train_test_split(X, Y, config, scaler) - y_pred, y_prob = machine_learning.fit_predict(X_train, y_train, X_test, clf) + y_pred, y_prob = machine_learning.fit_predict(X_train, y_train, X_test, clf, config) eval_dict = evaluation.evaluate_prediction(y_test, y_pred, y_prob, X_test, clf, config) y_df = conflict.get_pred_conflict_geometry(X_test_ID, X_test_geom, y_test, y_pred) X_df = pd.DataFrame(X_test) - - if not config.getboolean('general', 'verbose'): - sys.stdout = orig_stdout - f.close() - return X_df, y_df, eval_dict \ No newline at end of file + return X_df, y_df, eval_dict + +def predictive(X, scaler, config): + """Predictive model to use the already fitted classifier to make projections. + As other models, it reads data which are then scaled and used in conjuction with the classifier to project conflict risk. + + Args: + X (array): array containing the variable values plus unique identifer and geometry information. + scaler (scaler): the specified scaling method instance. + config (ConfigParser-object): object containing the parsed configuration-settings of the model. + + Raises: + ValueError: raised if path to pickled classifier is incorrect. + + Returns: + datatrame: containing model output on polygon-basis. + """ + + print('INFO: scaling the data from projection period') + X = pd.DataFrame(X) + if config.getboolean('general', 'verbose'): print('DEBUG: number of data points including missing values: {}'.format(len(X))) + X = X.dropna() + if config.getboolean('general', 'verbose'): print('DEBUG: number of data points excluding missing values: {}'.format(len(X))) + X_ID, X_geom, X_data = conflict.split_conflict_geom_data(X.to_numpy()) + ##- scaling only the variable values + X_ft = scaler.fit_transform(X_data) + + if os.path.isfile(os.path.abspath(config.get('pre_calc', 'clf'))): + with open(os.path.abspath(config.get('pre_calc', 'clf')), 'rb') as f: + print('INFO: loading classifier from {}'.format(os.path.abspath(config.get('pre_calc', 'clf')))) + clf = pickle.load(f) + else: + raise ValueError('ERROR: no pre-computed classifier specified in cfg-file, currently specified file {} does not exist'.format(os.path.abspath(config.get('pre_calc', 'clf')))) + + print('INFO: making the projection') + y_pred = clf.predict(X_ft) + arr = np.column_stack((X_ID, X_geom, y_pred)) + y_df = pd.DataFrame(arr, columns=['ID', 'geometry', 'y_pred']) + + return y_df \ No newline at end of file diff --git a/copro/pipeline.py b/copro/pipeline.py index 6cdb3c1..0bbe15c 100644 --- a/copro/pipeline.py +++ b/copro/pipeline.py @@ -4,7 +4,7 @@ import os, sys -def create_XY(config, conflict_gdf, polygon_gdf): +def create_XY(config, polygon_gdf, conflict_gdf): """Top-level function to create the X-array and Y-array. If the XY-data was pre-computed and specified in cfg-file, the data is loaded. If not, variable values and conflict data are read from file and stored in array. The resulting array is by default saved as npy-format to file. @@ -25,20 +25,36 @@ def create_XY(config, conflict_gdf, polygon_gdf): XY = data.fill_XY(XY, config, conflict_gdf, polygon_gdf) - if config.getboolean('general', 'verbose'): - print('saving XY data by default to file {}'.format(os.path.abspath(os.path.join(config.get('general', 'input_dir'), 'XY.npy'))) + os.linesep) - np.save(os.path.join(config.get('general', 'input_dir'),'XY'), XY) + print('INFO: saving XY data by default to file {}'.format(os.path.abspath(os.path.join(config.get('general', 'output_dir'), 'XY.npy')))) + np.save(os.path.join(config.get('general', 'output_dir'),'XY'), XY) else: - if config.getboolean('general', 'verbose'): - print('loading XY data from file {}'.format(os.path.abspath(os.path.join(config.get('general', 'input_dir'), config.get('pre_calc', 'XY')))) + os.linesep) - XY = np.load(os.path.join(config.get('general', 'input_dir'), config.get('pre_calc', 'XY')), allow_pickle=True) + print('INFO: loading XY data from file {}'.format(os.path.abspath(os.path.join(config.get('general', 'output_dir'), config.get('pre_calc', 'XY'))))) + XY = np.load(os.path.join(config.get('general', 'output_dir'), config.get('pre_calc', 'XY')), allow_pickle=True) X, Y = data.split_XY_data(XY, config) return X, Y +def create_X(config, polygon_gdf, conflict_gdf=None): + + if config.get('pre_calc', 'XY') is '': + + X = data.initiate_X_data(config) + + X = data.fill_XY(X, config, conflict_gdf, polygon_gdf) + + print('INFO: saving X data by default to file {}'.format(os.path.abspath(os.path.join(config.get('general', 'output_dir'), 'X.npy')))) + np.save(os.path.join(config.get('general', 'output_dir'),'X'), X) + + else: + + print('INFO: loading XY data from file {}'.format(os.path.abspath(config.get('pre_calc', 'X')))) + X = np.load(os.path.abspath(config.get('pre_calc', 'X')), allow_pickle=True) + + return X + def prepare_ML(config): """Top-level function to instantiate the scaler and model as specified in model configurations. @@ -56,7 +72,7 @@ def prepare_ML(config): return scaler, clf -def run(X, Y, config, scaler, clf, out_dir): +def run_reference(X, Y, config, scaler, clf, out_dir): """Top-level function to run one of the four supported models. Args: @@ -87,4 +103,26 @@ def run(X, Y, config, scaler, clf, out_dir): else: raise ValueError('the specified model type in the cfg-file is invalid - specify either 1, 2, 3 or 4.') - return X_df, y_df, eval_dict \ No newline at end of file + return X_df, y_df, eval_dict + +def run_prediction(X, scaler, config): + """Top-level function to run a predictive model with a already fitted classifier and new data. + + Args: + X (array): X-array containing variable values. + scaler (scaler): the specified scaler instance. + config (ConfigParser-object): object containing the parsed configuration-settings of the model. + + Raises: + ValueError: raised if another model type than the one using all data is specified in cfg-file. + + Returns: + datatrame: containing model output on polygon-basis. + """ + + if config.getint('general', 'model') != 1: + raise ValueError('ERROR: making a prediction is only possible with model type 1, i.e. using all data') + + y_df = models.predictive(X, scaler, config) + + return y_df \ No newline at end of file diff --git a/copro/plots.py b/copro/plots.py index a2e3c0d..85135bf 100644 --- a/copro/plots.py +++ b/copro/plots.py @@ -1,3 +1,5 @@ +import matplotlib +matplotlib.use('Agg') import matplotlib.pyplot as plt import geopandas as gpd import seaborn as sbs diff --git a/copro/selection.py b/copro/selection.py index 6645e6d..a2c870d 100644 --- a/copro/selection.py +++ b/copro/selection.py @@ -18,20 +18,20 @@ def filter_conflict_properties(gdf, config): selection_criteria = {'best': config.getint('conflict', 'min_nr_casualties'), 'type_of_violence': (config.get('conflict', 'type_of_violence')).rsplit(',')} - if config.getboolean('general', 'verbose'): print('filtering on conflict properties...') + print('INFO: filtering on conflict properties.') for key in selection_criteria: if selection_criteria[key] == '': - if config.getboolean('general', 'verbose'): print('...passing key', key, 'as it is empty') + if config.getboolean('general', 'verbose'): print('DEBUG: passing key', key, 'as it is empty') pass elif key == 'best': - if config.getboolean('general', 'verbose'): print('...filtering key', key, 'with lower value', selection_criteria[key]) + if config.getboolean('general', 'verbose'): print('DEBUG: filtering key', key, 'with lower value', selection_criteria[key]) gdf = gdf.loc[(gdf[key] >= selection_criteria[key])] else: - if config.getboolean('general', 'verbose'): print('...filtering key', key, 'with value(s)', selection_criteria[key]) + if config.getboolean('general', 'verbose'): print('DEBUG: filtering key', key, 'with value(s)', selection_criteria[key]) gdf = gdf.loc[(gdf[key].isin(selection_criteria[key]))] return gdf @@ -50,7 +50,7 @@ def select_period(gdf, config): t0 = config.getint('settings', 'y_start') t1 = config.getint('settings', 'y_end') - if config.getboolean('general', 'verbose'): print('focussing on period between {} and {}'.format(t0, t1)) + if config.getboolean('general', 'verbose'): print('DEBUG: focussing on period between {} and {}'.format(t0, t1)) gdf = gdf.loc[(gdf.year >= t0) & (gdf.year <= t1)] @@ -71,13 +71,13 @@ def clip_to_extent(gdf, config): shp_fo = os.path.join(os.path.abspath(config.get('general', 'input_dir')), config.get('extent', 'shp')) - if config.getboolean('general', 'verbose'): print('reading extent and spatial aggregation level from file {}'.format(shp_fo) + os.linesep) + print('INFO: reading extent and spatial aggregation level from file {}'.format(shp_fo)) extent_gdf = gpd.read_file(shp_fo) - if config.getboolean('general', 'verbose'): print('fixing invalid geometries' + os.linesep) + print('INFO: fixing invalid geometries') extent_gdf.geometry = extent_gdf.buffer(0) - if config.getboolean('general', 'verbose'): print('clipping clipping conflict dataset to extent' + os.linesep) + print('INFO: clipping clipping conflict dataset to extent') gdf = gpd.clip(gdf, extent_gdf) return gdf, extent_gdf @@ -122,10 +122,10 @@ def climate_zoning(gdf, extent_gdf, config): if KG_gdf.crs != 'EPSG:4326': KG_gdf = KG_gdf.to_crs('EPSG:4326') - if config.getboolean('general', 'verbose'): print('clipping conflicts to climate zones {}'.format(look_up_classes) + os.linesep) + if config.getboolean('general', 'verbose'): print('DEBUG: clipping conflicts to climate zones {}'.format(look_up_classes)) gdf = gpd.clip(gdf, KG_gdf.buffer(0)) - if config.getboolean('general', 'verbose'): print('clipping polygons to climate zones {}'.format(look_up_classes) + os.linesep) + if config.getboolean('general', 'verbose'): print('DEBUG: clipping polygons to climate zones {}'.format(look_up_classes)) polygon_gdf = gpd.clip(extent_gdf, KG_gdf.buffer(0)) elif config.get('climate', 'zones') == 'None': @@ -143,9 +143,11 @@ def climate_zoning(gdf, extent_gdf, config): def select(config, out_dir): """Main function performing the selection steps. + Also stores the selected conflicts and polygons to output directory. Args: config (ConfigParser-object): object containing the parsed configuration-settings of the model. + out_dir (str): path to output folder. Returns: geo-dataframe: remaining conflict data after selection process. diff --git a/copro/utils.py b/copro/utils.py index 0817221..4d9b749 100644 --- a/copro/utils.py +++ b/copro/utils.py @@ -7,6 +7,9 @@ from configparser import RawConfigParser from shutil import copyfile from sklearn import utils +from datetime import date +import click +import copro def get_geodataframe(config, longitude='longitude', latitude='latitude', crs='EPSG:4326'): """Georeferences a pandas dataframe using longitude and latitude columns of that dataframe. @@ -26,10 +29,10 @@ def get_geodataframe(config, longitude='longitude', latitude='latitude', crs='EP config.get('conflict', 'conflict_file')) # read file to pandas dataframe - if config.getboolean('general', 'verbose'): print('reading csv file to dataframe {}'.format(conflict_fo) + os.linesep) + print('INFO: reading csv file to dataframe {}'.format(conflict_fo)) df = pd.read_csv(conflict_fo) - if config.getboolean('general', 'verbose'): print('translating to geopandas dataframe' + os.linesep) + if config.getboolean('general', 'verbose'): print('DEBUG: translating to geopandas dataframe') gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[longitude], df[latitude]), crs=crs) @@ -95,9 +98,20 @@ def make_output_dir(config): """ out_dir = os.path.abspath(config.get('general','output_dir')) + print('INFO: saving output to folder {}'.format(out_dir)) + if not os.path.isdir(out_dir): os.makedirs(out_dir) - print('saving output to folder {}'.format(out_dir)) + else: + for root, dirs, files in os.walk(out_dir): + if config.getboolean('general', 'verbose'): print('DEBUG: remove files in folder {}'.format(os.path.abspath(root))) + for fo in files: + # print(fo) + if (fo == 'clf.pkl') or (fo =='XY.npy') or (fo == 'X.npy'): + if config.getboolean('general', 'verbose'): print('DEBUG: sparing {}'.format(fo)) + pass + else: + os.remove(os.path.join(root, fo)) return out_dir @@ -117,8 +131,7 @@ def download_PRIO(config): filename = os.path.join(path, 'ged201-csv.zip') - print('') - print('no conflict file was specified, hence downloading data from {} to {}'.format(url, filename) + os.linesep) + print('INFO: no conflict file was specified, hence downloading data from {} to {}'.format(url, filename)) urllib.request.urlretrieve(url, filename) @@ -132,6 +145,19 @@ def download_PRIO(config): return +def print_model_info(): + """Prints a header with main model information. + """ + + click.echo('') + click.echo(click.style('#### CoPro version {} ####'.format(copro.__version__), fg='yellow')) + click.echo(click.style('#### For information about the model, please visit https://copro.readthedocs.io/ ####', fg='yellow')) + click.echo(click.style('#### Copyright (2020-{}): {} ####'.format(date.today().year, copro.__author__), fg='yellow')) + click.echo(click.style('#### Contact via: {} ####'.format(copro.__email__), fg='yellow')) + click.echo(click.style('#### The model can be used and shared under the MIT license ####' + os.linesep, fg='yellow')) + + return + def initiate_setup(settings_file): """Initiates the model set-up. It parses the cfg-file, creates an output folder, copies the cfg-file to the output folder, and, if specified, downloads conflict data. @@ -141,10 +167,14 @@ def initiate_setup(settings_file): Returns: ConfigParser-object: parsed model configuration. out_dir: path to output folder - """ + """ + + print_model_info() config = parse_settings(settings_file) + print('INFO: verbose mode on: {}'.format(config.getboolean('general', 'verbose'))) + out_dir = make_output_dir(config) copyfile(settings_file, os.path.join(out_dir, 'copy_of_run_setting.cfg')) @@ -154,7 +184,7 @@ def initiate_setup(settings_file): if (config.getint('general', 'model') == 2) or (config.getint('general', 'model') == 3): config.set('settings', 'n_runs', str(1)) - print('changed nr of runs to {}'.format(config.getint('settings', 'n_runs'))) + print('INFOL changed nr of runs to {}'.format(config.getint('settings', 'n_runs'))) return config, out_dir diff --git a/copro/variables.py b/copro/variables.py index 792a9ad..0b98a76 100644 --- a/copro/variables.py +++ b/copro/variables.py @@ -4,9 +4,11 @@ import geopandas as gpd import rasterstats as rstats import numpy as np -import matplotlib.pyplot as plt import os, sys +import warnings +warnings.filterwarnings("ignore") + def nc_with_float_timestamp(extent_gdf, config, var_name, sim_year, stat_func='mean'): """This function extracts a statistical value from a netCDF-file (specified in the config-file) for each polygon specified in extent_gdf for a given year. By default, the mean value of all cells within a polygon is computed. @@ -36,9 +38,9 @@ def nc_with_float_timestamp(extent_gdf, config, var_name, sim_year, stat_func='m """ # get path to netCDF-file. nc_fo = os.path.join(os.path.abspath(config.get('general', 'input_dir')), - config.get('env_vars', var_name)) + config.get('data', var_name)) - if config.getboolean('general', 'verbose'): print('calculating mean {0} per aggregation unit from file {1} for year {2}'.format(var_name, nc_fo, sim_year)) + if config.getboolean('general', 'verbose'): print('DEBUG: calculating mean {0} per aggregation unit from file {1} for year {2}'.format(var_name, nc_fo, sim_year)) # open nc-file with xarray as dataset nc_ds = xr.open_dataset(nc_fo) @@ -60,8 +62,12 @@ def nc_with_float_timestamp(extent_gdf, config, var_name, sim_year, stat_func='m for i in range(len(extent_gdf)): prov = extent_gdf.iloc[i] zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats=stat_func) + if (zonal_stats[0][stat_func] == None) and (config.getboolean('general', 'verbose')): + print('WARNING: NaN computed!') list_out.append(zonal_stats[0][stat_func]) + if config.getboolean('general', 'verbose'): print('DEBUG: ... done.') + return list_out def nc_with_continous_datetime_timestamp(extent_gdf, config, var_name, sim_year, stat_func='mean'): @@ -91,9 +97,9 @@ def nc_with_continous_datetime_timestamp(extent_gdf, config, var_name, sim_year, """ # get path to netCDF-file. nc_fo = os.path.join(os.path.abspath(config.get('general', 'input_dir')), - config.get('env_vars', var_name)) + config.get('data', var_name)) - if config.getboolean('general', 'verbose'): print('calculating mean {0} per aggregation unit from file {1} for year {2}'.format(var_name, nc_fo, sim_year)) + if config.getboolean('general', 'verbose'): print('DEBUG: calculating mean {0} per aggregation unit from file {1} for year {2}'.format(var_name, nc_fo, sim_year)) # open nc-file with xarray as dataset nc_ds = xr.open_dataset(nc_fo) @@ -121,6 +127,10 @@ def nc_with_continous_datetime_timestamp(extent_gdf, config, var_name, sim_year, for i in range(len(extent_gdf)): prov = extent_gdf.iloc[i] zonal_stats = rstats.zonal_stats(prov.geometry, nc_arr_vals, affine=affine, stats=stat_func) + if (zonal_stats[0][stat_func] == None) and (config.getboolean('general', 'verbose')): + print('WARNING: NaN computed!') list_out.append(zonal_stats[0][stat_func]) + if config.getboolean('general', 'verbose'): print('DEBUG: ... done.') + return list_out \ No newline at end of file diff --git a/docs/Execution.rst b/docs/Execution.rst index aed5e8b..f5a174e 100644 --- a/docs/Execution.rst +++ b/docs/Execution.rst @@ -18,17 +18,51 @@ They can all be run and converted to htmls by executing the provided shell-scrip $ cd path/to/copro/example $ sh run.sh -It is of course also possible to execute the notebook cell by cell using jupyter notebook. +It is of course also possible to execute the notebook cell by cell using jupyter notebook. +The notebooks should give a fairly good impression how the model can be executed function-by-function and what functionality these functions have. + +.. _script: Runner script ---------------- To run the model from command line, a command line script is provided. -All data and settings are retrieved from the settings-file which needs to be provided as inline argument. +The number of inline arguments differs whether only a reference run or also one or more projections runs are executed. + +By default, output is stored to the output directory specified in the individual configurations-file (cfg-file). + +Reference run +^^^^^^^^^^^^^^^^ +All data and settings are retrieved from the cfg-file (see :ref:`settings`). +Based on these settings, data is sampled and the model is trained, tested, and evaluted. +The output is then stored to the output directory. .. code-block:: console $ cd path/to/copro/scripts $ python runner.py ../example/example_settings.cfg -By default, output is stored to the output directory specified in the settings-file. \ No newline at end of file +Projection runs +^^^^^^^^^^^^^^^^ +If also projections are computed, multiple additional cfg-files can be provided. +For each projection, one individual cfg-file is required. + +Since the projections are based on the reference run, at least two cfg-file are needed. +The command would then look like this: + +.. code-block:: console + + $ cd path/to/copro/scripts + $ python runner.py ../example/example_settings.cfg -proj ../example/example_settings_proj.cfg + +.. info:: + + Multiple projections can be made by specifing various cfg-files with the -proj flag. + +Help +^^^^^^^^^^^^^^^^ +For further help how to use the script, try this: + +.. code-block:: console + + $ python runner.py --help diff --git a/docs/Output.rst b/docs/Output.rst index eceacf6..6b1ff6d 100644 --- a/docs/Output.rst +++ b/docs/Output.rst @@ -1,7 +1,7 @@ Output ========================= -The model can produce a range of output files. Output is stored in the output folder. +The model can produce a range of output files. Output is stored in the output folder as specified in the configurations-file (cfg-file). .. note:: @@ -11,6 +11,11 @@ The model can produce a range of output files. Output is stored in the output fo Not all model types provide the output mentioned below. If the 'leave-one-out' or 'single variable' model are selected, only the metrics are stored to a csv-file. +.. important:: + + Most of the output can only be produced when running a reference model, i.e. when comparing the predictions against observations. + If running a prediction model, only the chance of conflict per polygon is stored to file. + Selected polygons ------------------ A shp-file named ``selected_polygons.shp`` contains all polygons after performing the selection procedure. @@ -19,27 +24,48 @@ Selected conflicts ------------------- The shp-file ``selected_conflicts.shp`` contains all conflict data points after performing the selection procedure. -Overall output ---------------- +Sampled variable and conflict data +----------------------------------- +During model execution, data is sampled per polygon and time step. +This data contains the geometry and ID of each polygon as well as unscaled variable values (X) and a boolean identifier whether conflict took place or not (Y). +If the model is re-run without making changes to the data and how it is sampled, the resulting XY-array is stored to ``XY.npy``. This file can be loaded again with ``np.load()``. + +If making projections, the Y-part is not available. The remaining X-data is still written to a file ``X.npy``. + +.. note:: + + Note that ``np.load()`` returns an array. This can be further processed with e.g. pandas. + +ML classifier +-------------- +At the end of a reference run, the chosen classifier is fitted with all available XY-data. +To be able to re-use the classifier (e.g. to make predictions), it is pickled to ``clf.pkl``. +All predictions +------------------ Per model run, a fraction of the total XY-data is used to make a prediction. -To be able to analyse model output, all predictions made per run are appended to a main output-file. -This file is, actually, the basis of all futher analyes. -Since this can become a rather large file, the dataframe is converted to npy-file (``out_y_df.npy``). This file can be loaded again with ``np.load()``. +To be able to analyse model output, all predictions (stored as pandas dataframes) made per run are appended to a main output-dataframe. +This dataframe is, actually, the basis of all futher analyes. +When storing to file, this can become a rather large file. +Therefore, the dataframe is converted to npy-file (``raw_output_data.npy``). This file can be loaded again with ``np.load()``. .. note:: Note that ``np.load()`` returns an array. This can be further processed with e.g. pandas. -Model accuracy per run +Evaluation metrics ----------------------- +Per model run, a range of metrics are computed to evalute the predictions made. +They are all appended to a dictionary and saved to the file ``evaluation_metrics.csv``. -Per model run, a range of metrics are computed. They are all appended to a dictionary and saved to the file ``out_dict.csv``. +ROC-AUC +-------- +To be able to determine the mean of the ROC-AUC score plus its standard deviation, the required data is stored to csv-files. +``ROC_data_tprs.csv`` contains the false positive rates per evaluation, and ``ROC_data_aucs.csv`` the area-under-curve values per run. -Model accuracy per polygon +Model prediction per polygon --------------------------- - -At the end of all model repetitions, the resulting output contains multiple predictions for each polygon. +At the end of all model repetitions, the resulting output dataframe contains multiple predictions for each polygon. By aggregating results per polygon, it is possible to assess model output spatially. Three main output metrics are calculated per polygon: @@ -48,24 +74,22 @@ Three main output metrics are calculated per polygon: 2. The total number of conflicts in the test (*NOC*); 3. The chance of conflict (*COC*), defined as the ration of number of conflict predictions to overall number of predictions made. +all data +^^^^^^^^^ + +All output metrics (CCP, NOC, COC) are determined based on the entire data set at the end of the run, i.e. without splitting it in chunks. + +The data is stored to ``output_per_polygon.shp``. + k-fold analysis ^^^^^^^^^^^^^^^^ - The model is repeated several times to eliminate the influence of how the data is split into training and test samples. As such, the accuracy per run and polygon will differ. To account for that, the resulting data set containing all predictions at the end of the run is split in k chunks. Subsequently, the mean, median, and standard deviation of CCP is determined from the k chunks. -The resulting shp-file is named ``kFold_CCP_stats.shp``. - -all data -^^^^^^^^^ - -All output metrics (CCP, NOC, COC) are determined based on the entire data set at the end of the run, i.e. without splitting it in chunks. - -The data is stored to ``all_stats.shp``. - +The resulting shp-file is named ``output_kFoldAnalysis_per_polygon.shp``. .. note:: diff --git a/docs/api/XYdata.rst b/docs/api/XYdata.rst index 48a34f1..58e892a 100644 --- a/docs/api/XYdata.rst +++ b/docs/api/XYdata.rst @@ -8,5 +8,6 @@ XY-Data :nosignatures: data.initiate_XY_data + data.initiate_X_data data.fill_XY data.split_XY_data \ No newline at end of file diff --git a/docs/api/evaluation.rst b/docs/api/evaluation.rst index dfc6b7a..8d2f0d2 100644 --- a/docs/api/evaluation.rst +++ b/docs/api/evaluation.rst @@ -14,6 +14,7 @@ Model evaluation evaluation.evaluate_prediction evaluation.polygon_model_accuracy evaluation.init_out_ROC_curve + evaluation.save_out_ROC_curve evaluation.categorize_polys evaluation.calc_kFold_polygon_analysis evaluation.get_feature_importance \ No newline at end of file diff --git a/docs/api/machine_learning.rst b/docs/api/machine_learning.rst index 3ae055f..86bf842 100644 --- a/docs/api/machine_learning.rst +++ b/docs/api/machine_learning.rst @@ -10,4 +10,5 @@ Machine learning machine_learning.define_scaling machine_learning.define_model machine_learning.split_scale_train_test_split - machine_learning.fit_predict \ No newline at end of file + machine_learning.fit_predict + machine_learning.pickle_clf \ No newline at end of file diff --git a/docs/api/models.rst b/docs/api/models.rst index f45e4ef..080b64b 100644 --- a/docs/api/models.rst +++ b/docs/api/models.rst @@ -11,7 +11,9 @@ The various models models.leave_one_out models.single_variables models.dubbelsteen + models.predictive .. note:: - the 'leave_one_out' and 'single_variables' models are only tested in beta-state. \ No newline at end of file + The 'leave_one_out' and 'single_variables' models are only tested in beta-state. + They will most likely be deprecated in near future. \ No newline at end of file diff --git a/docs/api/pipeline.rst b/docs/api/pipeline.rst index 450c537..3407dd2 100644 --- a/docs/api/pipeline.rst +++ b/docs/api/pipeline.rst @@ -9,4 +9,5 @@ The model pipeline pipeline.create_XY pipeline.prepare_ML - pipeline.run \ No newline at end of file + pipeline.run_reference + pipeline.run_prediction \ No newline at end of file diff --git a/docs/api/utils.rst b/docs/api/utils.rst index fe127e9..9f885e6 100644 --- a/docs/api/utils.rst +++ b/docs/api/utils.rst @@ -7,6 +7,7 @@ Auxiliary functions :toctree: generated/ :nosignatures: + utils.print_model_info utils.get_geodataframe utils.show_versions utils.parse_settings diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 7ad5f31..a034a89 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -4,6 +4,8 @@ Workflow ========= This page provides a short example workflow in Jupyter Notebooks. +It is designed such that the main features of model become clear. +Even though the model can be perfectly executed using notebooks, there is also the possibility to use a command line script (see :ref:`script`). .. toctree:: :maxdepth: 1 diff --git a/docs/examples/nb04_make_a_projection.ipynb.nblink b/docs/examples/nb04_make_a_projection.ipynb.nblink new file mode 100644 index 0000000..3268594 --- /dev/null +++ b/docs/examples/nb04_make_a_projection.ipynb.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../example/nb04_make_a_projection.ipynb" +} \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 2c680fb..d35dd41 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,8 +3,8 @@ copro This is the documentation website CoPro, a machine-learning tool for conflict risk projections based on climate, environmental, and societal drivers. -.. image:: https://travis-ci.com/JannisHoch/conflict_model.svg?token=BnX1oxxHRbyd1dPyXAp2&branch=dev - :target: https://travis-ci.com/JannisHoch/conflict_model +.. image:: https://travis-ci.com/JannisHoch/copro.svg?branch=dev + :target: https://travis-ci.com/JannisHoch/copro .. image:: https://img.shields.io/badge/License-MIT-blue.svg :target: https://github.com/JannisHoch/copro/blob/dev/LICENSE diff --git a/docs/model_settings.rst b/docs/model_settings.rst index b8a0abc..144ce13 100644 --- a/docs/model_settings.rst +++ b/docs/model_settings.rst @@ -1,3 +1,5 @@ +.. _settings: + Model settings ========================= @@ -67,7 +69,7 @@ Additionally, the cfg-file included in the GitHub-repository already contains th The code2class-file should not be altered except if another shp-file is provided containing undocumented climate classes. -**[env_vars]** +**[reference_data]** In this section, all variables to be used in the model need to be provided. The main convention is that the name of the file agrees with the variable name in the file. Only netCDF-files with annual data are supported. diff --git a/docs/requirements.rtd.txt b/docs/requirements.rtd.txt index 790d576..25b8a45 100644 --- a/docs/requirements.rtd.txt +++ b/docs/requirements.rtd.txt @@ -18,7 +18,7 @@ rasterio==1.1.3 rioxarray==0.0.26 xarray==0.15.0 ipython==7.13.0 -notebook==6.0.3 +notebook>=6.1.5 nbconvert==5.6.1 netCDF4==1.5.3 scikit-learn==0.22.1 diff --git a/environment.yml b/environment.yml index 1dafc3a..c32d6c0 100644 --- a/environment.yml +++ b/environment.yml @@ -24,6 +24,7 @@ dependencies: - click - seaborn==0.11.0 - pip + - colorama - pip: - rasterstats==0.14.0 \ No newline at end of file diff --git a/example/example_data/XY.npy b/example/example_data/XY.npy deleted file mode 100644 index a68e7ad..0000000 Binary files a/example/example_data/XY.npy and /dev/null differ diff --git a/example/example_settings.cfg b/example/example_settings.cfg index fa1e771..efea3fe 100644 --- a/example/example_settings.cfg +++ b/example/example_settings.cfg @@ -4,7 +4,7 @@ output_dir=../example/OUT # 1: all data; 2: leave-one-out model; 3: single variable model; 4: dubbelsteenmodel # Note that only 1 supports sensitivity_analysis model=1 -verbose=True +verbose=False [settings] # start year @@ -12,12 +12,15 @@ y_start=2000 # end year y_end=2015 # number of repetitions -n_runs=50 +n_runs=3 [pre_calc] -# if nothing is specified, the XY array will be stored in input_dir +# if nothing is specified, the XY array will be stored in output_dir # if XY already pre-calculated, then provide path to npy-file XY= +# if nothing is specified, the classifier will be stored in output_dir +# if classifier is already stored, then provide path to pkl-file +clf= [extent] shp=waterProvinces/waterProvinces_Africa.shp @@ -35,7 +38,7 @@ shp=KoeppenGeiger/2000/1976-2000.shp zones=BWh,BSh code2class=KoeppenGeiger/classification_codes.txt -[env_vars] +[data] # variable name here needs to be identical with variable name in nc-file total_evaporation=totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc precipitation=precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc diff --git a/example/example_settings_proj.cfg b/example/example_settings_proj.cfg new file mode 100644 index 0000000..50d08b9 --- /dev/null +++ b/example/example_settings_proj.cfg @@ -0,0 +1,53 @@ +[general] +input_dir=../example/example_data +output_dir=../example/OUT_PROJ +# 1: all data; 2: leave-one-out model; 3: single variable model; 4: dubbelsteenmodel +# Note that only 1 supports sensitivity_analysis +model=1 +verbose=False + +[settings] +# start year +y_start=2010 +# end year +y_end=2015 +# number of repetitions +n_runs=50 + +[pre_calc] +# if nothing is specified, the XY array will be stored in output_dir +# if XY already pre-calculated, then provide (absolute) path to npy-file +XY= +# if nothing is specified, the classifier will be stored in output_dir +# if classifier is already stored, then provide (absolute) path to pkl-file +clf=../example/OUT/clf.pkl + +[extent] +shp=waterProvinces/waterProvinces_Africa.shp + +[conflict] +# either specify path to file or state 'download' to download latest PRIO/UCDP dataset +conflict_file=download +min_nr_casualties=1 +# 1=state-based armed conflict; 2=non-state conflict; 3=one-sided violence +type_of_violence=1,2,3 + +[climate] +shp=KoeppenGeiger/2000/1976-2000.shp +# define either one or more classes (use abbreviations!) or specify None for not filtering +zones=BWh,BSh +code2class=KoeppenGeiger/classification_codes.txt + +[data] +# variable name here needs to be identical with variable name in nc-file +total_evaporation=totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc +precipitation=precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc +temperature=temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc +irr_water_demand=irrWaterDemand.nc + +[machine_learning] +# choose from: MinMaxScaler, StandardScaler, RobustScaler, QuantileTransformer +scaler=QuantileTransformer +# choose from: NuSVC, KNeighborsClassifier, RFClassifier +model=RFClassifier +train_fraction=0.7 \ No newline at end of file diff --git a/example/nb01_model_init_and_selection.ipynb b/example/nb01_model_init_and_selection.ipynb index b730624..2b857ad 100644 --- a/example/nb01_model_init_and_selection.ipynb +++ b/example/nb01_model_init_and_selection.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -48,7 +48,7 @@ "output_type": "stream", "text": [ "Python version: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 01:53:57) [MSC v.1916 64 bit (AMD64)]\n", - "copro version: 0.0.5\n", + "copro version: 0.0.6b\n", "geopandas version: 0.8.0\n", "xarray version: 0.15.1\n", "rasterio version: 1.1.0\n", @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -90,17 +90,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\n", - "\n", - "no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\r\n", - "\n" + "INFO: saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\n", + "INFO: no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\n" ] } ], @@ -121,31 +119,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "reading csv file to dataframe C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201.csv\n", - "\n", - "translating to geopandas dataframe\n", - "\n", - "filtering on conflict properties...\n", - "...filtering key best with lower value 1\n", - "...filtering key type_of_violence with value(s) ['1', '2', '3']\n", - "focussing on period between 2000 and 2015\n", - "reading extent and spatial aggregation level from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\waterProvinces/waterProvinces_Africa.shp\n", - "\n", - "fixing invalid geometries\n", - "\n", - "clipping clipping conflict dataset to extent\n", - "\n", - "clipping conflicts to climate zones ['BWh', 'BSh']\n", - "\n", - "clipping polygons to climate zones ['BWh', 'BSh']\n", - "\n" + "INFO: reading csv file to dataframe C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201.csv\n", + "DEBUG: translating to geopandas dataframe\n", + "INFO: filtering on conflict properties.\n", + "DEBUG: filtering key best with lower value 1\n", + "DEBUG: filtering key type_of_violence with value(s) ['1', '2', '3']\n", + "DEBUG: focussing on period between 2000 and 2015\n", + "INFO: reading extent and spatial aggregation level from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\waterProvinces/waterProvinces_Africa.shp\n", + "INFO: fixing invalid geometries\n", + "INFO: clipping clipping conflict dataset to extent\n", + "DEBUG: clipping conflicts to climate zones ['BWh', 'BSh']\n", + "DEBUG: clipping polygons to climate zones ['BWh', 'BSh']\n" ] } ], @@ -162,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -194,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ diff --git a/example/nb02_XY_data.ipynb b/example/nb02_XY_data.ipynb index a9aca50..991a76f 100644 --- a/example/nb02_XY_data.ipynb +++ b/example/nb02_XY_data.ipynb @@ -42,7 +42,7 @@ "output_type": "stream", "text": [ "Python version: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 01:53:57) [MSC v.1916 64 bit (AMD64)]\n", - "copro version: 0.0.5\n", + "copro version: 0.0.6b\n", "geopandas version: 0.8.0\n", "xarray version: 0.15.1\n", "rasterio version: 1.1.0\n", @@ -91,10 +91,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\n", "\n", - "no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\r\n", - "\n" + "#### CoPro version 0.0.6b ####\n", + "#### For information about the model, please visit https://copro.readthedocs.io/ ####\n", + "#### Copyright (2020): Jannis M. Hoch, Niko Wanders, Sophie de Bruin ####\n", + "#### Contact via: j.m.hoch@uu.nl ####\n", + "#### The model can be used and shared under the MIT license ####\n", + "\n", + "INFO: verbose mode on: True\n", + "INFO: saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\n", + "INFO: no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\n" ] } ], @@ -178,146 +184,119 @@ "text": [ "{'poly_ID': Series([], dtype: float64), 'poly_geometry': Series([], dtype: float64), 'total_evaporation': Series([], dtype: float64), 'precipitation': Series([], dtype: float64), 'temperature': Series([], dtype: float64), 'irr_water_demand': Series([], dtype: float64), 'conflict': Series([], dtype: int32)}\n", "\n", - "reading data for period from 2000 to 2015\n", - "\n", - "\n", - "entering year 2000\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2000\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2000\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2000\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2000\n", - "\n", - "entering year 2001\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2001\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2001\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2001\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2001\n", - "\n", - "entering year 2002\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2002\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2002\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2002\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2002\n", - "\n", - "entering year 2003\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2003\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2003\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2003\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2003\n", - "\n", - "entering year 2004\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2004\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2004\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2004\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2004\n", - "\n", - "entering year 2005\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2005\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2005\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2005\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2005\n", - "\n", - "entering year 2006\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2006\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2006\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2006\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2006\n", - "\n", - "entering year 2007\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2007\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2007\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2007\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2007\n", - "\n", - "entering year 2008\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2008\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2008\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2008\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2008\n", - "\n", - "entering year 2009\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2009\n" + "INFO: reading data for period from 2000 to 2015\n", + "INFO: entering year 2000\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2000\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2000\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2000\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2000\n", + "INFO: entering year 2001\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2001\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2001\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2001\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2001\n", + "INFO: entering year 2002\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2002\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2002\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2002\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2002\n", + "INFO: entering year 2003\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2003\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2003\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2003\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2003\n", + "INFO: entering year 2004\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2004\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2004\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2004\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2004\n", + "INFO: entering year 2005\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2005\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2005\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2005\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2005\n", + "INFO: entering year 2006\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2006\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2006\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2006\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2006\n", + "INFO: entering year 2007\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2007\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2007\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2007\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2007\n", + "INFO: entering year 2008\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2008\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2008\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2008\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2008\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2009\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2009\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2009\n", - "\n", - "entering year 2010\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2010\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2010\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2010\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2010\n", - "\n", - "entering year 2011\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2011\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2011\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2011\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2011\n", - "\n", - "entering year 2012\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2012\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2012\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2012\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2012\n", - "\n", - "entering year 2013\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2013\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2013\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2013\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2013\n", - "\n", - "entering year 2014\n", - "\n", - "listing the geometry of all geographical units\n", - "calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2014\n", - "calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2014\n", - "calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2014\n", - "calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2014\n", - "...reading data DONE\n", - "\n", - "saving XY data by default to file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\XY.npy\n", - "\n", - "number of data points including missing values: 4110\n", - "number of data points excluding missing values: 4005\n", - "from this, 619 points are equal to 1, i.e. represent conflict occurence. This is a fraction of 15.46 percent.\n" + "INFO: entering year 2009\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2009\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2009\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2009\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2009\n", + "INFO: entering year 2010\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2010\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2010\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2010\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2010\n", + "INFO: entering year 2011\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2011\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2011\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2011\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2011\n", + "INFO: entering year 2012\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2012\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2012\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2012\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2012\n", + "INFO: entering year 2013\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2013\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2013\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2013\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2013\n", + "INFO: entering year 2014\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2014\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2014\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2014\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2014\n", + "INFO: entering year 2015\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2015\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2015\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2015\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2015\n", + "INFO: all data read\n", + "INFO: saving XY data by default to file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\\XY.npy\n", + "DEBUG: number of data points including missing values: 4384\n", + "DEBUG: number of data points excluding missing values: 4272\n", + "DEBUG: a fraction of 15.94 percent in the data corresponds to conflicts.\n" ] } ], "source": [ - "X, Y = pipeline.create_XY(config, conflict_gdf, selected_polygons_gdf)" + "X, Y = pipeline.create_XY(config, selected_polygons_gdf, conflict_gdf)" ] }, { @@ -335,7 +314,7 @@ { "data": { "text/plain": [ - "True" + "False" ] }, "execution_count": 8, diff --git a/example/nb03_model_execution_and_evaluation.ipynb b/example/nb03_model_execution_and_evaluation.ipynb index b88263e..bc49105 100644 --- a/example/nb03_model_execution_and_evaluation.ipynb +++ b/example/nb03_model_execution_and_evaluation.ipynb @@ -13,7 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "from copro import utils, pipeline, evaluation, plots\n", + "from copro import utils, pipeline, evaluation, plots, machine_learning\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -43,7 +43,7 @@ "output_type": "stream", "text": [ "Python version: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 01:53:57) [MSC v.1916 64 bit (AMD64)]\n", - "copro version: 0.0.5\n", + "copro version: 0.0.6b\n", "geopandas version: 0.8.0\n", "xarray version: 0.15.1\n", "rasterio version: 1.1.0\n", @@ -99,2071 +99,228 @@ "name": "stdout", "output_type": "stream", "text": [ - "saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\n", "\n", - "no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\r\n", - "\n" - ] - } - ], - "source": [ - "config, out_dir = utils.initiate_setup(settings_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since no XY-data is specified in the config-file initially, we have to set this manually." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "config.set('pre_calc', 'XY', str(os.path.join(os.path.abspath(config.get('general', 'input_dir')), 'XY.npy')))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See if the right path pops up:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'C:\\\\Users\\\\hoch0001\\\\Documents\\\\_code\\\\copro\\\\example\\\\example_data\\\\XY.npy'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config.get('pre_calc', 'XY')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that this is taken care of, we also need to load in the data from the very first notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "conflict_gdf = gpd.read_file(os.path.join(out_dir, 'selected_conflicts.shp'))\n", - "selected_polygons_gdf = gpd.read_file(os.path.join(out_dir, 'selected_polygons.shp'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, for the conversion from numpy array to dataframe this requires a few more steps." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "global_arr = np.load(os.path.join(out_dir, 'global_df.npy'), allow_pickle=True)\n", - "global_df = pd.DataFrame(data=global_arr, columns=['geometry', 'ID'])\n", - "global_df.set_index(global_df.ID, inplace=True)\n", - "global_df.drop(['ID'] , axis=1, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The scence is set now and we can compute the X-array and Y-array in no time!" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading XY data from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\XY.npy\r\n", - "\n", - "number of data points including missing values: 4110\n", - "number of data points excluding missing values: 4005\n", - "from this, 619 points are equal to 1, i.e. represent conflict occurence. This is a fraction of 15.46 percent.\n" - ] - } - ], - "source": [ - "X, Y = pipeline.create_XY(config, conflict_gdf, selected_polygons_gdf)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scaler and classifier" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "chosen scaling method is QuantileTransformer()\n", - "chosen ML model is RandomForestClassifier(class_weight={1: 100}, n_estimators=1000)\n" - ] - } - ], - "source": [ - "scaler, clf = pipeline.prepare_ML(config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Output initialization\n", - "\n", - "Since the model is run multiple times, we need to initialize some stuff first to append the output per run." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "out_X_df = evaluation.init_out_df()\n", - "out_y_df = evaluation.init_out_df()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "out_dict = evaluation.init_out_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "trps, aucs, mean_fpr = evaluation.init_out_ROC_curve()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ML-model execution\n", - "\n", - "The pudels kern! This is where the magic happens, and not only once. Due make sure that any conincidental results are ruled out, we run the model multiple times. Thereby, always different samples are used for training and prediction. By using a sufficient number of runs and averaging the overall results, we should be able to get a good picture of what the model is capable of.\n", - "\n", - "The main evaluation metric is the mean ROC-score and [**ROC-curve**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_roc_curve.html), plotted at the end of all runs." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 1 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.846\n", - "Precision: 0.477\n", - "Recall: 0.289\n", - "F1 score: 0.360\n", - "Brier loss score: 0.107\n", - "Cohen-Kappa score: 0.278\n", - "ROC AUC score 0.813\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.94 0.91 1022\n", - " 1 0.48 0.29 0.36 180\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.68 0.62 0.64 1202\n", - "weighted avg 0.82 0.85 0.83 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 2 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.858\n", - "Precision: 0.593\n", - "Recall: 0.273\n", - "F1 score: 0.374\n", - "Brier loss score: 0.107\n", - "Cohen-Kappa score: 0.306\n", - "ROC AUC score 0.820\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.97 0.92 1015\n", - " 1 0.59 0.27 0.37 187\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.74 0.62 0.65 1202\n", - "weighted avg 0.83 0.86 0.83 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 3 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.846\n", - "Precision: 0.514\n", - "Recall: 0.203\n", - "F1 score: 0.291\n", - "Brier loss score: 0.108\n", - "Cohen-Kappa score: 0.223\n", - "ROC AUC score 0.807\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.96 0.91 1015\n", - " 1 0.51 0.20 0.29 187\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.69 0.58 0.60 1202\n", - "weighted avg 0.81 0.85 0.82 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 4 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.851\n", - "Precision: 0.543\n", - "Recall: 0.273\n", - "F1 score: 0.363\n", - "Brier loss score: 0.113\n", - "Cohen-Kappa score: 0.289\n", - "ROC AUC score 0.797\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.96 0.92 1015\n", - " 1 0.54 0.27 0.36 187\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.71 0.62 0.64 1202\n", - "weighted avg 0.83 0.85 0.83 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 5 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.857\n", - "Precision: 0.546\n", - "Recall: 0.293\n", - "F1 score: 0.381\n", - "Brier loss score: 0.102\n", - "Cohen-Kappa score: 0.309\n", - "ROC AUC score 0.828\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.96 0.92 1021\n", - " 1 0.55 0.29 0.38 181\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.72 0.62 0.65 1202\n", - "weighted avg 0.83 0.86 0.84 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 6 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.840\n", - "Precision: 0.549\n", - "Recall: 0.277\n", - "F1 score: 0.368\n", - "Brier loss score: 0.117\n", - "Cohen-Kappa score: 0.288\n", - "ROC AUC score 0.817\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.95 0.91 1000\n", - " 1 0.55 0.28 0.37 202\n", - "\n", - " accuracy 0.84 1202\n", - " macro avg 0.71 0.62 0.64 1202\n", - "weighted avg 0.81 0.84 0.82 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - 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"\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.862\n", - "Precision: 0.548\n", - "Recall: 0.291\n", - "F1 score: 0.381\n", - "Brier loss score: 0.099\n", - "Cohen-Kappa score: 0.311\n", - "ROC AUC score 0.837\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.89 0.96 0.92 1027\n", - " 1 0.55 0.29 0.38 175\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.72 0.63 0.65 1202\n", - "weighted avg 0.84 0.86 0.84 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 9 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.855\n", - "Precision: 0.608\n", - "Recall: 0.251\n", - "F1 score: 0.356\n", - "Brier loss score: 0.106\n", - "Cohen-Kappa score: 0.289\n", - "ROC AUC score 0.832\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.97 0.92 1011\n", - " 1 0.61 0.25 0.36 191\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.74 0.61 0.64 1202\n", - "weighted avg 0.83 0.86 0.83 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 10 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.859\n", - "Precision: 0.494\n", - "Recall: 0.249\n", - "F1 score: 0.331\n", - "Brier loss score: 0.101\n", - "Cohen-Kappa score: 0.261\n", - "ROC AUC score 0.821\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.89 0.96 0.92 1033\n", - " 1 0.49 0.25 0.33 169\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.69 0.60 0.63 1202\n", - "weighted avg 0.83 0.86 0.84 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 11 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.849\n", - "Precision: 0.510\n", - "Recall: 0.266\n", - "F1 score: 0.350\n", - "Brier loss score: 0.107\n", - "Cohen-Kappa score: 0.274\n", - "ROC AUC score 0.824\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.95 0.91 1018\n", - " 1 0.51 0.27 0.35 184\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.69 0.61 0.63 1202\n", - "weighted avg 0.82 0.85 0.83 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - 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"\n", - "Accuracy: 0.844\n", - "Precision: 0.581\n", - "Recall: 0.248\n", - "F1 score: 0.347\n", - "Brier loss score: 0.117\n", - "Cohen-Kappa score: 0.274\n", - "ROC AUC score 0.805\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.96 0.91 1000\n", - " 1 0.58 0.25 0.35 202\n", - "\n", - " accuracy 0.84 1202\n", - " macro avg 0.72 0.61 0.63 1202\n", - "weighted avg 0.82 0.84 0.82 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 14 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.860\n", - "Precision: 0.593\n", - "Recall: 0.277\n", - "F1 score: 0.378\n", - "Brier loss score: 0.102\n", - "Cohen-Kappa score: 0.311\n", - "ROC AUC score 0.832\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.97 0.92 1018\n", - " 1 0.59 0.28 0.38 184\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.74 0.62 0.65 1202\n", - "weighted avg 0.84 0.86 0.84 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 15 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.852\n", - "Precision: 0.513\n", - "Recall: 0.217\n", - "F1 score: 0.305\n", - "Brier loss score: 0.107\n", - "Cohen-Kappa score: 0.237\n", - "ROC AUC score 0.811\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.96 0.92 1022\n", - " 1 0.51 0.22 0.30 180\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.69 0.59 0.61 1202\n", - "weighted avg 0.82 0.85 0.83 1202\n", - "\n", - "\n" - ] - }, - { - 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"\n", - "Accuracy: 0.839\n", - "Precision: 0.511\n", - "Recall: 0.230\n", - "F1 score: 0.317\n", - "Brier loss score: 0.110\n", - "Cohen-Kappa score: 0.240\n", - "ROC AUC score 0.833\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.96 0.91 1006\n", - " 1 0.51 0.23 0.32 196\n", - "\n", - " accuracy 0.84 1202\n", - " macro avg 0.69 0.59 0.61 1202\n", - "weighted avg 0.81 0.84 0.81 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 41 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.853\n", - "Precision: 0.582\n", - "Recall: 0.242\n", - "F1 score: 0.342\n", - "Brier loss score: 0.101\n", - "Cohen-Kappa score: 0.275\n", - "ROC AUC score 0.849\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.97 0.92 1012\n", - " 1 0.58 0.24 0.34 190\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.73 0.60 0.63 1202\n", - "weighted avg 0.83 0.85 0.83 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 42 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.856\n", - "Precision: 0.562\n", - "Recall: 0.246\n", - "F1 score: 0.342\n", - "Brier loss score: 0.107\n", - "Cohen-Kappa score: 0.275\n", - "ROC AUC score 0.808\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.97 0.92 1019\n", - " 1 0.56 0.25 0.34 183\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.72 0.61 0.63 1202\n", - "weighted avg 0.83 0.86 0.83 1202\n", - "\n", - "\n" - ] - }, - { - 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"### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.862\n", - "Precision: 0.578\n", - "Recall: 0.268\n", - "F1 score: 0.366\n", - "Brier loss score: 0.105\n", - "Cohen-Kappa score: 0.300\n", - "ROC AUC score 0.812\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.97 0.92 1023\n", - " 1 0.58 0.27 0.37 179\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.73 0.62 0.64 1202\n", - "weighted avg 0.84 0.86 0.84 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 45 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.836\n", - "Precision: 0.550\n", - "Recall: 0.266\n", - "F1 score: 0.358\n", - "Brier loss score: 0.119\n", - "Cohen-Kappa score: 0.277\n", - "ROC AUC score 0.814\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.86 0.95 0.91 995\n", - " 1 0.55 0.27 0.36 207\n", - "\n", - " accuracy 0.84 1202\n", - " macro avg 0.71 0.61 0.63 1202\n", - "weighted avg 0.81 0.84 0.81 1202\n", - "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "run 46 of 50\n", + "#### CoPro version 0.0.6b ####\n", + "#### For information about the model, please visit https://copro.readthedocs.io/ ####\n", + "#### Copyright (2020): Jannis M. Hoch, Niko Wanders, Sophie de Bruin ####\n", + "#### Contact via: j.m.hoch@uu.nl ####\n", + "#### The model can be used and shared under the MIT license ####\n", "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.846\n", - "Precision: 0.573\n", - "Recall: 0.258\n", - "F1 score: 0.355\n", - "Brier loss score: 0.105\n", - "Cohen-Kappa score: 0.282\n", - "ROC AUC score 0.838\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.87 0.96 0.91 1004\n", - " 1 0.57 0.26 0.36 198\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.72 0.61 0.63 1202\n", - "weighted avg 0.82 0.85 0.82 1202\n", - "\n", - "\n" + "INFO: verbose mode on: True\n", + "INFO: saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\n", + "INFO: no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\n" ] - }, + } + ], + "source": [ + "config, out_dir = utils.initiate_setup(settings_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since no XY-data is specified in the config-file initially, we have to set this manually." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "config.set('pre_calc', 'XY', str(os.path.join(os.path.abspath(config.get('general', 'output_dir')), 'XY.npy')))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See if the right path pops up:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "No handles with labels found to put in legend.\n" - ] - }, + "data": { + "text/plain": [ + "'C:\\\\Users\\\\hoch0001\\\\Documents\\\\_code\\\\copro\\\\example\\\\OUT\\\\XY.npy'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config.get('pre_calc', 'XY')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that this is taken care of, we also need to load in the data from the very first notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "conflict_gdf = gpd.read_file(os.path.join(out_dir, 'selected_conflicts.shp'))\n", + "selected_polygons_gdf = gpd.read_file(os.path.join(out_dir, 'selected_polygons.shp'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, for the conversion from numpy array to dataframe this requires a few more steps." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "global_arr = np.load(os.path.join(out_dir, 'global_df.npy'), allow_pickle=True)\n", + "global_df = pd.DataFrame(data=global_arr, columns=['geometry', 'ID'])\n", + "global_df.set_index(global_df.ID, inplace=True)\n", + "global_df.drop(['ID'] , axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scence is set now and we can compute the X-array and Y-array in no time!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "run 47 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.864\n", - "Precision: 0.588\n", - "Recall: 0.331\n", - "F1 score: 0.424\n", - "Brier loss score: 0.104\n", - "Cohen-Kappa score: 0.354\n", - "ROC AUC score 0.803\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.89 0.96 0.92 1021\n", - " 1 0.59 0.33 0.42 181\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.74 0.65 0.67 1202\n", - "weighted avg 0.84 0.86 0.85 1202\n", - "\n", - "\n" + "INFO: loading XY data from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\\XY.npy\n", + "DEBUG: number of data points including missing values: 4384\n", + "DEBUG: number of data points excluding missing values: 4272\n", + "DEBUG: a fraction of 15.94 percent in the data corresponds to conflicts.\n" ] - }, + } + ], + "source": [ + "X, Y = pipeline.create_XY(config, selected_polygons_gdf, conflict_gdf, )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaler and classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "No handles with labels found to put in legend.\n" + "DEBUG: chosen scaling method is QuantileTransformer()\n", + "DEBUG: chosen ML model is RandomForestClassifier(class_weight={1: 100}, n_estimators=1000)\n" ] - }, + } + ], + "source": [ + "scaler, clf = pipeline.prepare_ML(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Output initialization\n", + "\n", + "Since the model is run multiple times, we need to initialize some stuff first to append the output per run." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "out_X_df = evaluation.init_out_df()\n", + "out_y_df = evaluation.init_out_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "out_dict = evaluation.init_out_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "trps, aucs, mean_fpr = evaluation.init_out_ROC_curve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ML-model execution\n", + "\n", + "The pudels kern! This is where the magic happens, and not only once. Due make sure that any conincidental results are ruled out, we run the model multiple times. Thereby, always different samples are used for training and prediction. By using a sufficient number of runs and averaging the overall results, we should be able to get a good picture of what the model is capable of.\n", + "\n", + "The main evaluation metric is the mean ROC-score and [**ROC-curve**](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_roc_curve.html), plotted at the end of all runs." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "run 48 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.864\n", - "Precision: 0.584\n", - "Recall: 0.254\n", - "F1 score: 0.354\n", - "Brier loss score: 0.104\n", - "Cohen-Kappa score: 0.291\n", - "ROC AUC score 0.812\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.97 0.92 1025\n", - " 1 0.58 0.25 0.35 177\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.73 0.61 0.64 1202\n", - "weighted avg 0.84 0.86 0.84 1202\n", - "\n", - "\n" + "INFO: run 1 of 3\n", + "INFO: using all data\n", + "DEBUG: fitting and transforming X\n", + "DEBUG: splitting both X and Y in train and test data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "... Accuracy: 0.849\n", + "... Precision: 0.589\n", + "... Recall: 0.296\n", + "... F1 score: 0.394\n", + "... Brier loss score: 0.117\n", + "... Cohen-Kappa score: 0.318\n", + "... ROC AUC score 0.795\n", "No handles with labels found to put in legend.\n" ] }, @@ -2171,38 +328,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "run 49 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.851\n", - "Precision: 0.466\n", - "Recall: 0.279\n", - "F1 score: 0.349\n", - "Brier loss score: 0.109\n", - "Cohen-Kappa score: 0.271\n", - "ROC AUC score 0.803\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.89 0.95 0.92 1030\n", - " 1 0.47 0.28 0.35 172\n", - "\n", - " accuracy 0.85 1202\n", - " macro avg 0.68 0.61 0.63 1202\n", - "weighted avg 0.83 0.85 0.83 1202\n", - "\n", - "\n" + "INFO: run 2 of 3\n", + "INFO: using all data\n", + "DEBUG: fitting and transforming X\n", + "DEBUG: splitting both X and Y in train and test data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "... Accuracy: 0.842\n", + "... Precision: 0.626\n", + "... Recall: 0.252\n", + "... F1 score: 0.360\n", + "... Brier loss score: 0.115\n", + "... Cohen-Kappa score: 0.288\n", + "... ROC AUC score 0.834\n", "No handles with labels found to put in legend.\n" ] }, @@ -2210,44 +352,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "run 50 of 50\n", - "\n", - "### USING ALL DATA ###\n", - "\n", - "fitting and transforming X\n", - "\n", - "splitting both X and Y in train and test data\n", - "\n", - "Accuracy: 0.859\n", - "Precision: 0.593\n", - "Recall: 0.261\n", - "F1 score: 0.362\n", - "Brier loss score: 0.105\n", - "Cohen-Kappa score: 0.296\n", - "ROC AUC score 0.830\n", - "\n", - " precision recall f1-score support\n", - "\n", - " 0 0.88 0.97 0.92 1018\n", - " 1 0.59 0.26 0.36 184\n", - "\n", - " accuracy 0.86 1202\n", - " macro avg 0.74 0.61 0.64 1202\n", - "weighted avg 0.83 0.86 0.84 1202\n", - "\n", - "\n" + "INFO: run 3 of 3\n", + "INFO: using all data\n", + "DEBUG: fitting and transforming X\n", + "DEBUG: splitting both X and Y in train and test data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "... Accuracy: 0.849\n", + "... Precision: 0.542\n", + "... Recall: 0.287\n", + "... F1 score: 0.375\n", + "... Brier loss score: 0.107\n", + "... Cohen-Kappa score: 0.299\n", + "... ROC AUC score 0.823\n", "No handles with labels found to put in legend.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2266,10 +393,10 @@ "for n in range(config.getint('settings', 'n_runs')):\n", " \n", " if config.getboolean('general', 'verbose'):\n", - " print('run {} of {}'.format(n+1, config.getint('settings', 'n_runs')) + os.linesep)\n", + " print('INFO: run {} of {}'.format(n+1, config.getint('settings', 'n_runs')))\n", "\n", " #- run machine learning model and return outputs\n", - " X_df, y_df, eval_dict = pipeline.run(X, Y, config, scaler, clf, out_dir)\n", + " X_df, y_df, eval_dict = pipeline.run_reference(X, Y, config, scaler, clf, out_dir)\n", " \n", " #- select sub-dataset with only datapoints with observed conflicts\n", " X1_df, y1_df = utils.get_conflict_datapoints_only(X_df, y_df)\n", @@ -2322,13 +449,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "average Accuracy of run with 50 repetitions is 0.854\n", - "average Precision of run with 50 repetitions is 0.556\n", - "average Recall of run with 50 repetitions is 0.267\n", - "average F1 score of run with 50 repetitions is 0.360\n", - "average Cohen-Kappa score of run with 50 repetitions is 0.289\n", - "average Brier loss score of run with 50 repetitions is 0.106\n", - "average ROC AUC score of run with 50 repetitions is 0.819\n" + "average Accuracy of run with 3 repetitions is 0.847\n", + "average Precision of run with 3 repetitions is 0.586\n", + "average Recall of run with 3 repetitions is 0.278\n", + "average F1 score of run with 3 repetitions is 0.376\n", + "average Cohen-Kappa score of run with 3 repetitions is 0.301\n", + "average Brier loss score of run with 3 repetitions is 0.113\n", + "average ROC AUC score of run with 3 repetitions is 0.817\n" ] } ], @@ -2345,7 +472,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2374,7 +501,7 @@ "outputs": [ { "data": { - "image/png": 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kqRJMaiRJKjGHn9qZ1EiSpEowqZEkqaQSl3Q3MqmRJEmVYFIjSVJZZe0GfKqxqJEkqcR89lM7h58kSVIlmNRIklRSiUu6G5nUSJKkSjCpkSSptHxMQiOLGkmSSszVT+0cfpIkSZVgUiNJUok5UbidSY0kSaoEkxpJkkoq06SmkUWNJEkl5uqndg4/SZKkSjCpkSSpxFzS3c6kRpIkVYJJjSRJJeZE4XYWNZIklVQSFjUNHH6SJEmVYFIjSVKJOU+4nUmNJEmqBJMaSZLKyjsKb8akRpIkVYJJjSRJZeakmjqLGkmSSszhp3YOP0mSpEowqZEkqcR89lM7kxpJklQJFjWSJJVUUptT09tbVyJip4iYGxF3R8T9EfGZon1MRFwfEY8UP0c3HHNmRCyMiIci4siG9gMj4t7is7MjIor2wRFxWdF+e0Ts0VW/LGokSSqrBDJ6f+vaeuAtmflaYH9gZkQcApwB3JCZ04AbivdExD7ALGBfYCbwrYgYWJzr28DJwLRim1m0nwSszsy9gK8CX+yqUxY1kiSpR7JmXfF2h2JL4CjgoqL9IuDo4vVRwKWZuT4zFwELgYMiYiIwMjNvy8wELu5wTNu5fgoc3pbibI1FjSRJJZbZ+xswLiLmNWwnd7xuRAyMiLuA5cD1mXk7MCEzl9X6lcuAXYrdJwFPNBy+pGibVLzu2L7ZMZm5CXgWGNvZd+HqJ0mS1NGKzJze2Q6Z2QLsHxE7A5dHxH6d7L6lhCU7ae/smK0yqZEkqcyyCVtPLp/5DHATtbkwTxVDShQ/lxe7LQGmNBw2GVhatE/eQvtmx0TEIGAUsKqzvljUSJJUWr2/8qmbq5/GFwkNETEEOAJ4ELgSOLHY7UTgiuL1lcCsYkXTVGoTgucWQ1RrI+KQYr7MCR2OaTvXscCNxbybrXL4SZIk9dRE4KJiBdMAYE5m/k9E3AbMiYiTgMXAcQCZeX9EzAEWAJuAU4vhK4BTgAuBIcDVxQZwHnBJRCykltDM6qpTFjWSJJVZH9xRODPvAQ7YQvtK4PCtHDMbmL2F9nnAS+bjZOaLFEVRdzn8JEmSKsGkRpKkskqf0t3IpEaSJFWCSY0kSWXmU7rrLGokSSo1h5/aOPwkSZIqwaRGkqQyc/ipzqRGkiRVgkmNJEllZlJTZ1EjSVJZJeB9auocfpIkSZVgUiNJUol1/tzql5etFjUR8Q06GanLzNOa0iNJkqRt0FlSM2+79UKSJG0bk5q6rRY1mXlR4/uIGJaZzzW/S5IkqducKFzX5UThiDg0IhYADxTvXxsR32p6zyRJknqgO6ufvgYcCawEyMy7gTc2s1OSJKl7Int/K6tuLenOzCc6NLU0oS+SJEnbrDtLup+IiDcAGRE7AqdRDEVJkqQ+lDhRuEF3kpoPAKcCk4A/A/sX7yVJkvqNLpOazFwBvHc79EWSJPVIuPqpQXdWP70iIq6KiKcjYnlEXBERr9genZMkSV3IJmwl1Z3hpx8Bc4CJwG7AT4AfN7NTkiRJPdWdoiYy85LM3FRsP6DUdZwkSRViUlPX2bOfxhQvfxMRZwCXUvtV/xb45XbomyRJUrd1NlF4PrUipm0G0vsbPkvgc83qlCRJ6qYSJyu9rbNnP03dnh2RJEk9lLj6qUF3br5HROwH7APs1NaWmRc3q1OSJEk91WVRExGfAmZQK2p+Bbwd+B1gUSNJUh8r87Oaelt3Vj8dCxwOPJmZ/wC8Fhjc1F5JkiT1UHeGn17IzNaI2BQRI4HlgDffkySpPzCpqetOUTMvInYGvkdtRdQ6YG5TeyVJktRD3Xn20weLl9+JiGuAkZl5T3O7JUmS1DOd3XzvdZ19lpl3NqdLkiSpu5wo3K6zpOYrnXyWwFt6uS88ct9w3j7tsN4+raTCgNdM6esuSJUWD/++r7vwstbZzffevD07IkmStoE336vrzpJuSZKkfq9bdxSWJEn9UMmfqt3bLGokSSozi5q6LoefouZ9EfH/ive7R8RBze+aJElS93VnTs23gEOB44v3a4FvNq1HkiSp2yJ7fyur7gw/HZyZr4uIPwJk5uqI2LHJ/ZIkSeqR7hQ1GyNiIMWoXUSMB1qb2itJktQ9JU5Welt3hp/OBi4HdomI2cDvgM83tVeSJKl7sglbSXXn2U8/jIj5wOFAAEdn5gNN75kkSVIPdFnURMTuwPPAVY1tmbm4mR2TJEmdK/vE3t7WnTk1v6QWRgWwEzAVeAjYt4n9kiRJ6pHuDD+9uvF98fTu9zetR5Ikqft89lNdj+8onJl3RsTrm9EZSZLUQw4/1XVnTs0/N7wdALwOeLppPZIkSdoG3UlqRjS83kRtjs3PmtMdSZLUE04UbtdpUVPcdG94Zv7bduqPJEnSNtlqURMRgzJzUzExWJIk9UcmNXWdJTVzqc2fuSsirgR+AjzX9mFm/rzJfZMkSeq27sypGQOsBN5C+/1qErCokSSpL3nzvc10VtTsUqx8uo/2YqaNX6EkSf2B/0Wu66yoGQgMZ/Nipo1foSRJ6lc6K2qWZeZnt1tPJElSzxkz1A3o5DPvuyxJkkqjs6Tm8O3WC0mStE2cKNxuq0lNZq7anh2RJEn6S3Q2/CRJklQaPX5KtyRJ6kccfqozqZEkSZVgUiNJUll5R+HNWNRIklRmFjV1Dj9JkqRKMKmRJKnMTGrqTGokSVIlmNRIklRSgROFG5nUSJKkSjCpkSSpzExq6ixqJEkqK+9TsxmHnyRJUiWY1EiSVGYmNXUmNZIkqRJMaiRJKjOTmjqLGkmSSsyJwu0cfpIkST0SEVMi4jcR8UBE3B8RpxftYyLi+oh4pPg5uuGYMyNiYUQ8FBFHNrQfGBH3Fp+dHRFRtA+OiMuK9tsjYo+u+mVRI0lSmWUTtq5tAv4lM18FHAKcGhH7AGcAN2TmNOCG4j3FZ7OAfYGZwLciYmBxrm8DJwPTim1m0X4SsDoz9wK+Cnyxq05Z1EiSpB7JzGWZeWfxei3wADAJOAq4qNjtIuDo4vVRwKWZuT4zFwELgYMiYiIwMjNvy8wELu5wTNu5fgoc3pbibI1FjSRJZdWMlKaHc3SKYaEDgNuBCZm5DGqFD7BLsdsk4ImGw5YUbZOK1x3bNzsmMzcBzwJjO+uLE4UlSSqxJk0UHhcR8xren5uZ577k2hHDgZ8BH8nMNZ0EKVv6IDtp7+yYrbKokSRJHa3IzOmd7RARO1AraH6YmT8vmp+KiImZuawYWlpetC8BpjQcPhlYWrRP3kJ74zFLImIQMApY1VmfHH6SJKnM+mD4qZjbch7wQGb+d8NHVwInFq9PBK5oaJ9VrGiaSm1C8NxiiGptRBxSnPOEDse0netY4MZi3s1WmdRIkqSeOgz4O+DeiLiraPs4cBYwJyJOAhYDxwFk5v0RMQdYQG3l1KmZ2VIcdwpwITAEuLrYoFY0XRIRC6klNLO66pRFjSRJJdYXN9/LzN+x5TkvAIdv5ZjZwOwttM8D9ttC+4sURVF3OfwkSZIqwaRGkqQy8zEJdRY1kiSV1TbcV6bKHH6SJEmVYFIjSVJJBVufrftyZFIjSZIqwaRGkqQyc05NnUWNJEkl1hf3qemvHH6SJEmVYFIjSVKZmdTUmdRIkqRKMKmRJKnMTGrqLGokSSqrdKJwI4efJElSJZjUSJJUZiY1dSY1kiSpEkxqJEkqMefUtDOpkSRJlWBSI0lSmZnU1FnUSJJUYg4/tXP4SZIkVYJJjSRJZZU4/NTApEaSJFWCSY0kSWVmUlNnUSNJUkkFThRu5PCTJEmqBJMaSZLKzKSmzqRGkiRVgkmNJEklFmlU08aiRpKksvI+NZtx+EmSJFWCSY0kSSXmku52JjWSJKkSTGokSSozk5o6ixpJkkrM4ad2Dj9JkqRKMKmRJKnMTGrqTGokSVIlmNRIklRW6ZyaRiY1kiSpEkxqJEkqM5OaOosaSZJKKnD4qZHDT5IkqRJMaiRJKrM0qmljUiNJkirBpEaSpBJzTk07ixpJksoqcfVTA4efJElSJZjUSJJUYtHa1z3oP0xqJElSJZjUSJJUZs6pqbOokSSpxFz91M6iRnXjdl3Pv375EUaP30i2wtWXTeCKi3Zj+KiNnPn1h5kwaT1P/XkwXzhtb9atGcSgHVr58OceZdp+z5Gt8J3/nMq9c0cBcOJH/8ThxzzN8JGbeNf+h/Txbyb1H5Mmr+HMM2+rv5+46zouuWQ/Vqwcyvvedx9TpqzhI6e/lUceGVPfZ4+pz3DaafMYOnQjra3B6ae9lQEDko9/4lYmTlxHa2tw+x9244ILXtsXv5LUbzStqImI84F3Asszc79mXUe9p6Ul+N4X9uDRBcMZMqyFsy+/mz/+fmeOeNdy7rp1FD85dzLHnbyEd79/Ced/eQ9mvvspAD74zv0ZNWYDnzvvAU5/12vIDG7/zRiu/MFEzrv+zj7+raT+5c9LRvKhU48EYMCAVi75wVXceutkBg9u4XOfO4zTTpu32f4DBrTysY/9gS9/6WAWLRrNiBHraWkJBgxIfvbTvbnnngkMGtTCF866ienTlzFv3sS++LXUVxLvKNygmROFLwRmNvH86mWrn96RRxcMB+CF5wbyxKNDGDthA4cevopfX74LAL++fBcOPWIVALvv9QJ33bozAM+u2pHn1gxi2qvXAfDgXSNY/fSOffBbSOWx//7LWbZsGMuXD+OJJ0by5yUjX7LPgQc+yaJFO7No0WgA1q4dTGvrANavH8Q990wAYNOmgSxcOJpx457frv2X+pumFTWZeQuwqlnnV3PtMulF9tznOR66ezg7j9tYL1BWP70jo8ZuBGDRg0M59IhVDBiYTJj8Invtt47xEzf0ZbelUnnTmxZz801/1ek+kyatJRP+c/bNfOOcazn22Adess+wYRs4+OCl3HXXhGZ1Vf1YZO9vZdXnc2oi4mTgZICdYlgf90YAOw1t4ZPnPMR3Z0/l+XVb/7/ItT+dwJQ9X+Dsy+9m+Z8H88CdI2jZFNuxp1J5DRrUwsGH/JkLLnhNp/sNHJjsu+8KTj/traxfP5AvnHUTCxeOqRcwAwa08u9n3MaVV0zjySeHb4+uS/1Wnxc1mXkucC7AqIHjSlwfVsPAQa188pyH+M2V47n1urEAPLNiB0aP38Dqp3dk9PgNPLtyBwBaW4JzPz+1fuxXLruXpX/aqU/6LZXN9OlP8ujC0TzzTOd/ZlasGMq9945nzZrBANxxx0T23Gt1vag5/fR5LF06gl/8Yu+m91n9lP/lrPPme2qQfOTzj/LEo0O4/ILd6q1/uHEMRxyzHIAjjlnObTfUVmUM3qmFwUNaADjgsGdoaQkWLxy6/bstldCMGX/ippt273K/+fN3ZerUZxg8eBMDBrTy6lc/zeLFtbk3J5x4L0OHbeS73zmg2d1VPxU4/NSoz5Ma9R/7HriWI455mkUPDuWcK+8C4KKv/BVzvjuJj3/9YY48bjlPLx3M7NNeCcCosRuZff4CWjNY+eSO/Ne/7lU/1z9+7HHe/L9XMHhIK5f8dh7XzNmFH36j67/ApZeDwYM3ccDrnuLss6fX297whiWccsqdjBq1ns989hYee2w0n/zEm1i3bkd+/vO9+frZ15MJd9yxG3fM3Y1x457n+OMXsHjxCL5xznUAXHXVXlx7zZ599WtJfS6ySUvBIuLHwAxgHPAU8KnMPK+zY0YNHJeHDH1nU/ojCdhzSl/3QKq0Pzx8Hs8+v3S7TS4csfPk3H/G6b1+3t9d8bH5mTm96z37l6YlNZl5fLPOLUmS1JHDT5IklViZ58D0NosaSZLKzKKmztVPkiSpEkxqJEkqMYef2pnUSJKkSjCpkSSprBJoNappY1EjSVKZWdPUOfwkSZIqwaRGkqQSc6JwO5MaSZJUCSY1kiSVWZOe4VhGJjWSJKkSTGokSSox59S0s6iRJKmsEpd0N3D4SZIkVYJJjSRJJRVAOFG4zqRGkiRVgkWNJEll1tqErQsRcX5ELI+I+xraxkTE9RHxSPFzdMNnZ0bEwoh4KCKObGg/MCLuLT47OyKiaB8cEZcV7bdHxB7d+SosaiRJKrHI7PWtGy4EZnZoOwO4ITOnATcU74mIfYBZwL7FMd+KiIHFMd8GTgamFVvbOU8CVvX2mu4AAAiNSURBVGfmXsBXgS92p1MWNZIkqUcy8xZgVYfmo4CLitcXAUc3tF+ameszcxGwEDgoIiYCIzPztsxM4OIOx7Sd66fA4W0pTmcsaiRJKqts0gbjImJew3ZyN3ozITOXARQ/dynaJwFPNOy3pGibVLzu2L7ZMZm5CXgWGNtVB1z9JEmSOlqRmdN76VxbSliyk/bOjumUSY0kSaWVtWc/9fa2bZ4qhpQofi4v2pcAUxr2mwwsLdonb6F9s2MiYhAwipcOd72ERY0kSSUW2fvbNroSOLF4fSJwRUP7rGJF01RqE4LnFkNUayPikGK+zAkdjmk717HAjcW8m045/CRJknokIn4MzKA292YJ8CngLGBORJwELAaOA8jM+yNiDrAA2AScmpktxalOobaSaghwdbEBnAdcEhELqSU0s7rTL4saSZLKrA/uKJyZx2/lo8O3sv9sYPYW2ucB+22h/UWKoqgnHH6SJEmVYFIjSVJZJUQ37gD8cmFSI0mSKsGkRpKkMvMp3XUWNZIklZk1TZ3DT5IkqRJMaiRJKrFuPlX7ZcGkRpIkVYJJjSRJZWZSU2dRI0lSWSXgfWrqHH6SJEmVYFIjSVJJBelE4QYmNZIkqRJMaiRJKjOTmjqLGkmSysyips7hJ0mSVAkmNZIklZVLujdjUiNJkirBpEaSpBJzSXc7kxpJklQJJjWSJJWZSU2dRY0kSaWVFjUNHH6SJEmVYFIjSVJZJSY1DUxqJElSJZjUSJJUZt58r86iRpKkEvM+Ne0cfpIkSZVgUiNJUpmZ1NSZ1EiSpEowqZEkqawSaDWpaWNRI0lSaXlH4UYOP0mSpEowqZEkqcxMaupMaiRJUiWY1EiSVGYmNXUmNZIkqRJMaiRJKiuXdG/GokaSpNJKSJ9o2cbhJ0mSVAkmNZIklZkThetMaiRJUiWY1EiSVFZOFN6MRY0kSWXm8FOdw0+SJKkSTGokSSozk5o6kxpJklQJJjWSJJVWmtQ0sKiRJKmsEmj1jsJtHH6SJEmVYFIjSVKZOfxUZ1IjSZIqwaRGkqQyM6mpM6mRJEmVYFIjSVJppc9+amBRI0lSWSVkuqS7jcNPkiSpEkxqJEkqM4ef6kxqJElSJZjUSJJUZi7prrOokSSprDJ99lMDh58kSVIlmNRIklRmDj/VmdRIkqRKMKmRJKnE0jk1dRY1kiSVVjr81MDhJ0mSVAkmNZIklVXiHYUbmNRIkqRKMKmRJKnMfEp3nUmNJEmqBJMaSZJKKoF0Tk2dRY0kSWWV6fBTA4efJElSJZjUSJJUYg4/tTOpkSRJlWBSI0lSmTmnpi6yHz0zIiKeBv7U1/1Qt40DVvR1J6SK889ZufxVZo7fXheLiGuo/X+kt63IzJlNOG9T9auiRuUSEfMyc3pf90OqMv+cSd3nnBpJklQJFjWSJKkSLGr0lzi3rzsgvQz450zqJufUSJKkSjCpkSRJlWBRo20SETMj4qGIWBgRZ/R1f6SqiYjzI2J5RNzX132RysKiRj0WEQOBbwJvB/YBjo+Iffq2V1LlXAiU7j4hUl+yqNG2OAhYmJmPZeYG4FLgqD7uk1QpmXkLsKqv+yGViUWNtsUk4ImG90uKNkmS+oxFjbZFbKHNZXSSpD5lUaNtsQSY0vB+MrC0j/oiSRJgUaNtcwcwLSKmRsSOwCzgyj7ukyTpZc6iRj2WmZuADwHXAg8AczLz/r7tlVQtEfFj4DZg74hYEhEn9XWfpP7OOwpLkqRKMKmRJEmVYFEjSZIqwaJGkiRVgkWNJEmqBIsaSZJUCRY1UhNFREtE3BUR90XETyJi6F9wrgsj4tji9fc7e4hoRMyIiDdswzUej4hx3W3vsM+6Hl7r0xHxrz3toyRtjUWN1FwvZOb+mbkfsAH4QOOHxRPPeywz/ykzF3Syywygx0WNJJWZRY20/fwW2KtIUX4TET8C7o2IgRHx5Yi4IyLuiYj3A0TNORGxICJ+CezSdqKIuCkiphevZ0bEnRFxd0TcEBF7UCuePlqkRP8rIsZHxM+Ka9wREYcVx46NiOsi4o8R8V22/FyvzUTELyJifkTcHxEnd/jsK0VfboiI8UXbnhFxTXHMbyPir3vjy5Skjgb1dQekl4OIGAS8HbimaDoI2C8zFxWFwbOZ+fqIGAz8PiKuAw4A9gZeDUwAFgDndzjveOB7wBuLc43JzFUR8R1gXWb+V7Hfj4CvZubvImJ3aneDfhXwKeB3mfnZiPgbYLMiZSv+sbjGEOCOiPhZZq4EhgF3Zua/RMT/K879IeBc4AOZ+UhEHAx8C3jLNnyNktQpixqpuYZExF3F698C51EbFpqbmYuK9rcBr2mbLwOMAqYBbwR+nJktwNKIuHEL5z8EuKXtXJm5aiv9OALYJ6IexIyMiBHFNd5VHPvLiFjdjd/ptIg4png9pejrSqAVuKxo/wHw84gYXvy+P2m49uBuXEOSesyiRmquFzJz/8aG4j/uzzU2AR/OzGs77PcOoKvnmEQ39oHaUPOhmfnCFvrS7WelRMQMagXSoZn5fETcBOy0ld2zuO4zHb8DSWoG59RIfe9a4JSI2AEgIl4ZEcOAW4BZxZybicCbt3DsbcCbImJqceyYon0tMKJhv+uoDQVR7NdWZNwCvLdoezswuou+jgJWFwXNX1NLitoMANrSpvdQG9ZaAyyKiOOKa0REvLaLa0jSNrGokfre96nNl7kzIu4DvkstRb0ceAS4F/g2cHPHAzPzaWrzYH4eEXfTPvxzFXBM20Rh4DRgejEReQHtq7A+A7wxIu6kNgy2uIu+XgMMioh7gM8Bf2j47Dlg34iYT23OzGeL9vcCJxX9ux84qhvfiST1mE/pliRJlWBSI0mSKsGiRpIkVYJFjSRJqgSLGkmSVAkWNZIkqRIsaiRJUiVY1EiSpEqwqJEkSZXw/wF9TTe6vqf4VAAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -2422,7 +549,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2486,7 +613,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2503,9 +630,9 @@ " legend_kwds={'label': \"chance of correct predictions\", 'orientation': \"horizontal\"})\n", "selected_polygons_gdf.boundary.plot(ax=ax1, color='0.5')\n", "gdf_hit.plot(ax=ax2, column='nr_test_confl', legend=True, cmap='Reds', \n", - " legend_kwds={'label': \"nr of conflicts per polygon\", 'orientation': \"horizontal\"})\n", + " legend_kwds={'label': \"nr of pred. conflicts per polygon\", 'orientation': \"horizontal\"})\n", "selected_polygons_gdf.boundary.plot(ax=ax2, color='0.5')\n", - "gdf_hit.plot(ax=ax3, column='chance_correct_confl_pred', legend=True, cmap='Blues', \n", + "gdf_hit.plot(ax=ax3, column='chance_correct_confl_pred', legend=True, cmap='Blues', vmin=0, vmax=1, \n", " legend_kwds={'label': \"chance of conflict\", 'orientation': \"horizontal\"})\n", "selected_polygons_gdf.boundary.plot(ax=ax3, color='0.5');" ] @@ -2533,7 +660,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -2639,17 +766,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Last, we can determine the relative importance of each feature, that is variable." + "## Preparing for the future" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we have trained and tested our model with various combinations of data. Subsequently, the average performance of the model was evaluated with a range of metrics.\n", + "\n", + "If we want to re-use our model for the future and want to make predictions, it is necessary to save the model (that is, the classifier). It can then be loaded and prediction can be made with other variable values than those used for this reference run." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: fitting the classifier with all data from reference period\n", + "DEBUG: loading XY data from C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\\XY.npy\n", + "DEBUG: number of data points including missing values: 4384\n", + "DEBUG: number of data points excluding missing values: 4272\n", + "DEBUG: a fraction of 15.94 percent in the data corresponds to conflicts.\n", + "INFO: dumping classifier to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\\clf.pkl\n" + ] + } + ], + "source": [ + "clf = machine_learning.pickle_clf(scaler, clf, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With that, we can also determine the relative importance of each feature based on all data." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/example/nb04_make_a_projection.ipynb b/example/nb04_make_a_projection.ipynb new file mode 100644 index 0000000..d1eaee7 --- /dev/null +++ b/example/nb04_make_a_projection.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Projection conflict risk" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from copro import utils, pipeline, evaluation, plots\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import seaborn as sbs\n", + "import os, sys\n", + "from sklearn import metrics\n", + "import warnings\n", + "warnings.simplefilter(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For better reproducibility, the version numbers of all key packages are provided." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python version: 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 01:53:57) [MSC v.1916 64 bit (AMD64)]\n", + "copro version: 0.0.6b\n", + "geopandas version: 0.8.0\n", + "xarray version: 0.15.1\n", + "rasterio version: 1.1.0\n", + "pandas version: 1.0.3\n", + "numpy version: 1.18.1\n", + "scikit-learn version: 0.23.2\n", + "matplotlib version: 3.2.1\n", + "seaborn version: 0.11.0\n", + "rasterstats version: 0.14.0\n" + ] + } + ], + "source": [ + "utils.show_versions()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting the scence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cfg-file, all the settings for the analysis are defined. By 'parsing' (i.e. reading) it, all settings and file paths are known to the model. This is a simple way to make the code independent of the input data and settings." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "settings_file = 'example_settings_proj.cfg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on this cfg-file, the set-up of the run can be initialized. One part of the cfg-file is the specification and creation of an output folder." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "#### CoPro version 0.0.6b ####\n", + "#### For information about the model, please visit https://copro.readthedocs.io/ ####\n", + "#### Copyright (2020): Jannis M. Hoch, Niko Wanders, Sophie de Bruin ####\n", + "#### Contact via: j.m.hoch@uu.nl ####\n", + "#### The model can be used and shared under the MIT license ####\n", + "\n", + "INFO: verbose mode on: True\n", + "INFO: saving output to folder C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT_PROJ\n", + "INFO: no conflict file was specified, hence downloading data from http://ucdp.uu.se/downloads/ged/ged201-csv.zip to C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\UCDP\\ged201-csv.zip\n" + ] + } + ], + "source": [ + "config, out_dir = utils.initiate_setup(settings_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to load in the selected polygons from the very first notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "selected_polygons_gdf = gpd.read_file(r'./OUT/selected_polygons.shp')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, for the conversion from numpy array to dataframe this requires a few more steps." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "global_arr = np.load(r'./OUT/global_df.npy', allow_pickle=True)\n", + "global_df = pd.DataFrame(data=global_arr, columns=['geometry', 'ID'])\n", + "global_df.set_index(global_df.ID, inplace=True)\n", + "global_df.drop(['ID'] , axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data from files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar as in the second notebook, we need to collect the variable values for the projection time period. This time, however, we do not need to check whether conflict took place in a polygon as we are now in the projection mode and will not train and test the model again." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'poly_ID': Series([], dtype: float64), 'poly_geometry': Series([], dtype: float64), 'total_evaporation': Series([], dtype: float64), 'precipitation': Series([], dtype: float64), 'temperature': Series([], dtype: float64), 'irr_water_demand': Series([], dtype: float64)}\n", + "\n", + "INFO: reading data for period from 2010 to 2015\n", + "INFO: entering year 2010\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2010\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2010\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2010\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2010\n", + "INFO: entering year 2011\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2011\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2011\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2011\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2011\n", + "INFO: entering year 2012\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2012\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2012\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2012\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2012\n", + "INFO: entering year 2013\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2013\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2013\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2013\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2013\n", + "INFO: entering year 2014\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2014\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2014\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2014\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2014\n", + "INFO: entering year 2015\n", + "DEBUG: getting the geometry of all geographical units\n", + "DEBUG: calculating mean total_evaporation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\totalEvaporation_monthTot_output_2000_2015_Africa_yearmean.nc for year 2015\n", + "DEBUG: calculating mean precipitation per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\precipitation_monthTot_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2015\n", + "DEBUG: calculating mean temperature per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\temperature_monthAvg_output_2000-01-31_to_2015-12-31_Africa_yearmean.nc for year 2015\n", + "DEBUG: calculating mean irr_water_demand per aggregation unit from file C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\example_data\\irrWaterDemand.nc for year 2015\n", + "INFO: all data read\n" + ] + } + ], + "source": [ + "X = pipeline.create_X(config, selected_polygons_gdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scaler and classifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we need to scale this data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEBUG: chosen scaling method is QuantileTransformer()\n", + "DEBUG: chosen ML model is RandomForestClassifier(class_weight={1: 100}, n_estimators=1000)\n" + ] + } + ], + "source": [ + "scaler, clf = pipeline.prepare_ML(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note, however, that the classifier will have to be loaded from a pickled-file as we have to use a trained classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predict!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this all in place, we can use the data, the scaler, and the trained classifier to make a prediction whether conflict will take place in a polygon or not. This step still includes multiple predictions per polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: scaling the data from projection period\n", + "DEBUG: number of data points including missing values: 1644\n", + "DEBUG: number of data points excluding missing values: 1602\n", + "INFO: loading classifier from C:\\Users\\hoch0001\\Documents\\_code\\copro\\example\\OUT\\clf.pkl\n", + "INFO: making the projection\n" + ] + } + ], + "source": [ + "y_df = pipeline.run_prediction(X, scaler, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the fancy evaluation metrics are not applicable anymore, as we don't have observed data for the future projections. We can still look which fraction of predictions actually predicted conflict for a given polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df_hit, gdf_hit = evaluation.polygon_model_accuracy(y_df, global_df, out_dir=None, make_proj=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And this is how it looks like:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = gdf_hit.plot(column='chance_correct_confl_pred', legend=True, figsize=(20, 10), cmap='Blues', vmin=0, vmax=1,\n", + " legend_kwds={'label': \"chance of conflict\", 'orientation': \"vertical\"})\n", + "selected_polygons_gdf.boundary.plot(ax=ax, color='0.5');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that this plot may be different than a similar plot in the previous notebook. This is not unlikely as we use a different amount and different predictions in general here! For instance, we did not repeat the predictions multiple times, nor did we split the data points in training data and test data." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements_dev.txt b/requirements_dev.txt index f50385c..044bc92 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -7,12 +7,13 @@ ipython==7.13.0 matplotlib==3.2.1 nbconvert==5.6.1 netcdf4==1.5.3 -notebook==6.0.3 +notebook>=6.1.5 numpy==1.18.1 pandas==1.0.3 +pyproj==2.6.0 pytest==5.4.2 pytest-runner==5.2 -rasterio>=1.1.0 +rasterio==1.1.0 rioxarray==0.0.26 scikit-learn==0.22.1 sphinx==3.0.3 @@ -20,5 +21,5 @@ xarray==0.15.1 flake8==3.7.8 tox==3.14.0 coverage==4.5.4 -rasterstats==0.14. +rasterstats==0.14 seaborn==0.10.1 \ No newline at end of file diff --git a/scripts/postprocessing/plot_polygon_vals.py b/scripts/postprocessing/plot_polygon_vals.py new file mode 100644 index 0000000..00033c3 --- /dev/null +++ b/scripts/postprocessing/plot_polygon_vals.py @@ -0,0 +1,45 @@ +import click +import matplotlib.pyplot as plt +import geopandas as gpd +import os + +@click.command() +@click.option('-s', '--shp-file',help='path to shp file') +@click.option('-c', '--column', help='column name') +@click.option('-t', '--title', help='title for plot and file name') +@click.option('-v0', '--minimum-value') +@click.option('-v1', '--maximum-value') +@click.option('-o', '--output-dir', help='path to output directory', type=click.Path()) + +def main(shp_file=None, column=None, title=None, minimum_value=None, maximum_value=None, output_dir=None): + """Quick and dirty function to plot the column values of a shape file with minimum user input, and save plot. + """ + + world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) + africa = world[world.continent=='Africa'] + + fo = os.path.abspath(shp_file) + click.echo('\nreading shp-file {}'.format(fo)) + click.echo('... and converting it to geopandas dataframe') + df = gpd.read_file(fo) + + click.echo('plotting column {}'.format(column)) + fig, ax = plt.subplots(1, 1, figsize=(10,10)) + df.plot(column=column, + ax=ax, + cmap='Reds', + vmin=minimum_value, vmax=maximum_value, + legend=True, + legend_kwds={'label': str(column), + 'orientation': "vertical"}) + ax.set_title(str(title), fontsize=20) + + africa.boundary.plot(ax=ax, color='0.5') + + file_name = 'plt_{0}_fromFile_{1}_andColumn_{2}.png'.format(title, shp_file.rsplit('/')[-1], column) + click.echo('saving plot to file {}'.format(os.path.abspath(os.path.join(output_dir, file_name)))) + plt.savefig(os.path.abspath(os.path.join(output_dir, file_name)), dpi=300, bbox_inches='tight') + +if __name__ == '__main__': + + main() \ No newline at end of file diff --git a/scripts/run_projections.sh b/scripts/run_projections.sh new file mode 100644 index 0000000..d48a580 --- /dev/null +++ b/scripts/run_projections.sh @@ -0,0 +1 @@ +python runner.py ../example/example_settings.cfg -proj ../example/example_settings_proj.cfg diff --git a/scripts/run_reference.sh b/scripts/run_reference.sh new file mode 100644 index 0000000..df9a72d --- /dev/null +++ b/scripts/run_reference.sh @@ -0,0 +1 @@ +python runner.py ../example/example_settings.cfg \ No newline at end of file diff --git a/scripts/run_script.sh b/scripts/run_script.sh deleted file mode 100644 index 62d9d84..0000000 --- a/scripts/run_script.sh +++ /dev/null @@ -1 +0,0 @@ -python runner.py ../example/example_settings.cfg diff --git a/scripts/runner.py b/scripts/runner.py index 97a0835..aa6756d 100644 --- a/scripts/runner.py +++ b/scripts/runner.py @@ -7,8 +7,7 @@ import matplotlib.pyplot as plt import warnings -warnings.filterwarnings("module") - +warnings.filterwarnings("ignore") @click.group() def cli(): @@ -16,22 +15,26 @@ def cli(): @click.command() @click.argument('cfg', type=click.Path()) +@click.option('--projection-settings', '-proj', help='path to cfg-file with settings for a projection run', multiple=True, type=click.Path()) +@click.option('--verbose', '-v', help='command line switch to turn on verbose mode', is_flag=True) -def main(cfg): - """Main command line script to execute the model. All settings are read from cfg-file. +def main(cfg, projection_settings=[], verbose=False): + """Main command line script to execute the model. + All settings are read from cfg-file. + One cfg-file is required argument to train, test, and evaluate the model. + Additional cfg-files can be provided as optional arguments, whereby each file corresponds to one projection to be made. Args: CFG (str): (relative) path to cfg-file - """ - - print('') - print('#### CONFLICT MODEL version {} ####'.format(copro.__version__)) - print('') - + """ + #- parsing settings-file and getting path to output folder config, out_dir = copro.utils.initiate_setup(cfg) - print('verbose mode on: {}'.format(config.getboolean('general', 'verbose')) + os.linesep) + if verbose: + config.set('general', 'verbose', str(verbose)) + + click.echo(click.style('\nINFO: reference run started\n', fg='cyan')) #- selecting conflicts and getting area-of-interest and aggregation level conflict_gdf, extent_gdf, extent_active_polys_gdf, global_df = copro.selection.select(config, out_dir) @@ -43,7 +46,7 @@ def main(cfg): #- create X and Y arrays by reading conflict and variable files; #- or by loading a pre-computed array (npy-file) - X, Y = copro.pipeline.create_XY(config, conflict_gdf, extent_active_polys_gdf) + X, Y = copro.pipeline.create_XY(config, extent_active_polys_gdf, conflict_gdf) #- defining scaling and model algorithms scaler, clf = copro.pipeline.prepare_ML(config) @@ -58,14 +61,14 @@ def main(cfg): #- create plot instance for ROC plots fig, ax1 = plt.subplots(1, 1, figsize=(20,10)) + click.echo('INFO: training and testing machine learning model') #- go through all n model executions for n in range(config.getint('settings', 'n_runs')): - if config.getboolean('general', 'verbose'): - print('run {} of {}'.format(n+1, config.getint('settings', 'n_runs')) + os.linesep) + click.echo('INFO: run {} of {}'.format(n+1, config.getint('settings', 'n_runs'))) #- run machine learning model and return outputs - X_df, y_df, eval_dict = copro.pipeline.run(X, Y, config, scaler, clf, out_dir) + X_df, y_df, eval_dict = copro.pipeline.run_reference(X, Y, config, scaler, clf, out_dir) #- append per model execution #TODO: put all this into one function @@ -81,31 +84,50 @@ def main(cfg): copro.plots.plot_ROC_curve_n_mean(ax1, tprs, aucs, mean_fpr) #- save plot plt.savefig(os.path.join(out_dir, 'ROC_curve_per_run.png'), dpi=300, bbox_inches='tight') + #- save data for plot + copro.evaluation.save_out_ROC_curve(tprs, aucs, out_dir) #- save output dictionary to csv-file - copro.utils.save_to_csv(out_dict, out_dir, 'out_dict') - copro.utils.save_to_npy(out_y_df, out_dir, 'out_y_df') + copro.utils.save_to_csv(out_dict, out_dir, 'evaluation_metrics') + copro.utils.save_to_npy(out_y_df, out_dir, 'raw_output_data') #- print mean values of all evaluation metrics for key in out_dict: if config.getboolean('general', 'verbose'): - print('average {0} of run with {1} repetitions is {2:0.3f}'.format(key, config.getint('settings', 'n_runs'), np.mean(out_dict[key]))) + click.echo('DEBUG: average {0} of run with {1} repetitions is {2:0.3f}'.format(key, config.getint('settings', 'n_runs'), np.mean(out_dict[key]))) + + # create accuracy values per polygon and save to output folder + df_hit, gdf_hit = copro.evaluation.polygon_model_accuracy(out_y_df, global_df, out_dir) - df_hit, gdf_hit = copro.evaluation.polygon_model_accuracy(out_y_df, global_df, out_dir=None) + # apply k-fold + gdf_CCP = copro.evaluation.calc_kFold_polygon_analysis(out_y_df, global_df, out_dir, k=10) #- plot distribution of all evaluation metrics fig, ax = plt.subplots(1, 1) copro.plots.metrics_distribution(out_dict, figsize=(20, 10)) plt.savefig(os.path.join(out_dir, 'metrics_distribution.png'), dpi=300, bbox_inches='tight') - #- plot relative importance of each feature + clf = copro.machine_learning.pickle_clf(scaler, clf, config) + #- plot relative importance of each feature based on ALL data points fig, ax = plt.subplots(1, 1) - copro.plots.factor_importance(clf, config, ax=ax, figsize=(20, 10)) - plt.savefig(os.path.join(out_dir, 'factor_importance.png'), dpi=300, bbox_inches='tight') + copro.plots.factor_importance(clf, config, out_dir=out_dir, ax=ax, figsize=(20, 10)) + plt.savefig(os.path.join(out_dir, 'feature_importances.png'), dpi=300, bbox_inches='tight') - fig, ax = plt.subplots(1, 1) - copro.plots.polygon_categorization(gdf_hit, ax=ax) - plt.savefig(os.path.join(out_dir, 'polygon_categorization.png'), dpi=300, bbox_inches='tight') + click.echo('INFO: reference run succesfully finished') + + if projection_settings is not []: + + for proj in projection_settings: + + click.echo(click.style('\nINFO: projection run started, based on {}'.format(os.path.abspath(proj)), fg='cyan')) + + config, out_dir = copro.utils.initiate_setup(proj) + + X = copro.pipeline.create_X(config, extent_active_polys_gdf) + + y_df = copro.pipeline.run_prediction(X, scaler, config) + + df_hit, gdf_hit = copro.evaluation.polygon_model_accuracy(y_df, global_df, out_dir=out_dir, make_proj=True) if __name__ == '__main__': main() \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index 1550173..03c88ff 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.0.5 +current_version = 0.0.6 commit = True tag = True diff --git a/setup.py b/setup.py index bccb6cb..a3c6d8c 100644 --- a/setup.py +++ b/setup.py @@ -45,6 +45,6 @@ test_suite='tests', tests_require=test_requirements, url='https://github.com/JannisHoch/copro', - version='0.0.5', + version='0.0.6', zip_safe=False, ) diff --git a/tests/test_conflict.py b/tests/test_conflict.py index 976a65a..a53e705 100644 --- a/tests/test_conflict.py +++ b/tests/test_conflict.py @@ -5,6 +5,15 @@ import geopandas as gpd from copro import conflict +def create_fake_config(): + + config = configparser.ConfigParser() + + config.add_section('general') + config.set('general', 'verbose', str(False)) + + return config + def test_split_conflict_geom_data(): #TODO: would like to do this with actual geometry information, but np.equal() does not like this... @@ -27,7 +36,9 @@ def test_get_poly_geometry(): gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) - list_geometry = conflict.get_poly_geometry(gdf) + config = create_fake_config() + + list_geometry = conflict.get_poly_geometry(gdf, config) assert len(gdf) == len(list_geometry)