Add regression metric reporter and update requirements

datasist/__init__.py

@@ -4,4 +4,3 @@
 from . import timeseries
 from . import visualizations
 from . import model
-from . import nlp

datasist/model.py

@@ -4,7 +4,7 @@
 '''
 import platform
 
-from sklearn.metrics import roc_curve, confusion_matrix, precision_score, accuracy_score, recall_score, f1_score, make_scorer
+from sklearn.metrics import roc_curve, confusion_matrix, precision_score, accuracy_score, recall_score, f1_score, make_scorer, mean_absolute_error, mean_squared_error, r2_score, mean_squared_log_error
 from sklearn.model_selection import KFold, cross_val_score
 import numpy as np
 import pandas as pd
@@ -232,6 +232,55 @@ def get_classification_report(y_train=None, prediction=None, show_roc_plot=True,
             plt.savefig("roc_plot.png")
 
 
+def get_regression_report(y_true=None, prediction=None, show_r2_plot=True, save_plot=False):
+    '''
+    Generates performance report for a regression problem.
+
+    Parameters:
+    ------------------
+    y_true: Array, series, list.
+
+        The truth/ground value from the train data set.
+    
+    prediction: Array, series, list.
+
+        The predicted value by a trained model.
+
+    show_r2_plot: Bool, default True.
+
+        Show the r-squared curve.
+
+    save_plot: Bool, default True.
+
+        Save the plot to the current working directory.
+
+    '''
+    mae = mean_absolute_error(y_true, prediction)
+    mse = mean_squared_error(y_true, prediction)
+    msle = precision_score(y_true, prediction)
+    r2 = r2_score(y_true, prediction)
+
+    print("Mean Absolute Error: ", round(mae, 5))
+    print("Mean Squared Error: ", round(mse, 5))
+    print("Mean Squared Log Error: ", round(msle, 5))
+    print("R-squared Error:  ", round(r2, 5))
+    print("*" * 100)
+
+    if show_r2_plot:              
+        plt.scatter(y_true,prediction)
+        plt.xlabel('Truth values')
+        plt.ylabel('Predicted values')
+        plt.plot(np.unique(y_true), np.poly1d(np.polyfit(y_true, y_true, 1))(np.unique(y_true)))
+        plt.text(0.7, 0.2, 'R-squared = %0.2f' % r2)
+        plt.show()
+
+        if save_plot:
+            plt.savefig("r2_plot.png")
+
+
+
+
+
 def compare_model(models_list=None, x_train=None, y_train=None, scoring_metric=None, scoring_cv=3, silenced=True, plot=True):
     """
     Train multiple user-defined model and display report based on defined metric. Enables user to pick the best base model for a problem.

datasist/tests/test_model.py

@@ -6,6 +6,7 @@
 
 from datasist import model
 
+
 def test_compare_model_classification():
     x_train, y_train = make_classification(
         n_samples=50, 
@@ -24,6 +25,7 @@ def test_compare_model_classification():
     assert type(model_scores) is list
     assert hasattr(fitted_model[0], "predict")
 
+
 def test_compare_model_regression():
     x_train, y_train = make_classification(
         n_samples=50, 

docs/index.html

@@ -29,7 +29,7 @@ <h1 class="title">Module <code>datasist</code></h1>
 from . import timeseries
 from . import visualizations
 from . import model
-from . import nlp</code></pre>
+</code></pre>
 </details>
 </section>
 <section>
@@ -43,10 +43,7 @@ <h2 class="section-title" id="header-submodules">Sub-modules</h2>
 <dd>
 <section class="desc"><p>This module contains all functions relating to modeling in using sklearn library.</p></section>
 </dd>
-<dt><code class="name"><a title="datasist.nlp" href="nlp.html">datasist.nlp</a></code></dt>
-<dd>
-<section class="desc"><p>This module contains all functions relating to nlp</p></section>
-</dd>
+<dd></dd>
 <dt><code class="name"><a title="datasist.structdata" href="structdata.html">datasist.structdata</a></code></dt>
 <dd>
 <section class="desc"><p>This module contains all functions relating to the cleaning and exploration of structured data sets; mostly in pandas format</p></section>
@@ -80,7 +77,6 @@ <h1><img src="datasist.png" alt="logo"></h1>
 <ul>
 <li><code><a title="datasist.feature_engineering" href="feature_engineering.html">datasist.feature_engineering</a></code></li>
 <li><code><a title="datasist.model" href="model.html">datasist.model</a></code></li>
-<li><code><a title="datasist.nlp" href="nlp.html">datasist.nlp</a></code></li>
 <li><code><a title="datasist.structdata" href="structdata.html">datasist.structdata</a></code></li>
 <li><code><a title="datasist.timeseries" href="timeseries.html">datasist.timeseries</a></code></li>
 <li><code><a title="datasist.visualizations" href="visualizations.html">datasist.visualizations</a></code></li>

requirements.txt

@@ -4,6 +4,4 @@ pandas
 matplotlib
 seaborn
 scikit-learn
-numpy
-spacy
-en
+numpy