furhter cleanups

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 9
       matrix:
-        python-version: [3.7, 3.8]
+        python-version: [3.9, 3.10, 3.11]
         os: [ubuntu-latest, macos-latest]
 
     steps:

talos/autom8/autoparams.py

@@ -168,7 +168,7 @@ def batch_size(self, min_size=8, max_size=None, steps=None):
         integer value at the same time.'''
 
         if max_size is None and steps is None:
-            values = [int(np.exp2(i/2)) for i in range(3, 15)]
+            values = [int(np.exp2(i / 2)) for i in range(3, 15)]
         else:
             values = list(range(min_size, max_size, steps))
 
@@ -180,7 +180,7 @@ def epochs(self, min_epochs=50, max_epochs=None, steps=None):
         integer value at the same time.'''
 
         if max_epochs is None and steps is None:
-            values = [int(np.exp2(i/2))+50 for i in range(3, 15)]
+            values = [int(np.exp2(i / 2)) + 50 for i in range(3, 15)]
         else:
             values = list(range(min_epochs, max_epochs, steps))
 

talos/callbacks/experiment_log.py

@@ -27,7 +27,7 @@ def __init__(self,
         try:
             latest_file = max(list_of_files, key=os.path.getmtime)
         except ValueError:
-            print("\n TALOS ERROR: `experiment_name` has to match `Scan(experiment_name)`\n")
+            print("\nERROR: `experiment_name` has to match `Scan(experiment_name)`\n")
 
         self.name = latest_file.replace('.csv', '') + '.log'
 

talos/templates/datasets.py

@@ -18,7 +18,9 @@ def telco_churn(quantile=.5):
     import wrangle
     import pandas as pd
 
-    df = pd.read_csv('https://raw.githubusercontent.com/autonomio/examples/master/telco_churn/telco_churn_for_sensitivity.csv')
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'examples/master/telco_churn/telco_churn_for_sensitivity.csv'
+    df = pd.read_csv(base_url + url)
 
     df = df.drop(['val_acc', 'loss', 'f1score', 'acc', 'round_epochs'], axis=1)
 
@@ -42,8 +44,10 @@ def telco_churn(quantile=.5):
 def icu_mortality(samples=None):
 
     import pandas as pd
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'icu_mortality.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'icu_mortality.csv')
     df = df.dropna(thresh=3580, axis=1)
     df = df.dropna()
     df = df.sample(frac=1).head(samples)
@@ -56,8 +60,10 @@ def icu_mortality(samples=None):
 def titanic():
 
     import pandas as pd
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'titanic.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'titanic.csv')
 
     y = df.survived.values
 
@@ -81,8 +87,10 @@ def iris():
 
     import pandas as pd
     from tensorflow.keras.utils import to_categorical
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'iris.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'iris.csv')
     df['species'] = df['species'].factorize()[0]
     df = df.sample(len(df))
     y = to_categorical(df['species'])
@@ -98,8 +106,10 @@ def cervical_cancer():
 
     import pandas as pd
     from numpy import nan
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'cervical_cancer.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'cervical_cancer.csv')
     df = df.replace('?', nan)
     df = df.drop(['citology', 'hinselmann', 'biopsy'], axis=1)
     df = df.drop(['since_first_diagnosis',
@@ -116,8 +126,10 @@ def cervical_cancer():
 def breast_cancer():
 
     import pandas as pd
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'breast_cancer.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'breast_cancer.csv')
 
     # then some minimal data cleanup
     df.drop("Unnamed: 32", axis=1, inplace=True)

talos/templates/params.py

@@ -18,16 +18,16 @@ def titanic(debug=False):
     if debug:
 
         p = {'lr': [0.1, 0.2],
-            'first_neuron': [4, 8],
-            'batch_size': [20, 30],
-            'dropout': [0.2, 0.3],
-            'optimizer': [Adam(), Nadam()],
-            'epochs': [50, 100],
-            'losses': ['logcosh', 'binary_crossentropy'],
-            'shapes': ['brick', 'triangle', 0.2],
-            'hidden_layers': [0, 1],
-            'activation': ['relu', 'elu'],
-            'last_activation': ['sigmoid']}
+             'first_neuron': [4, 8],
+             'batch_size': [20, 30],
+             'dropout': [0.2, 0.3],
+             'optimizer': [Adam(), Nadam()],
+             'epochs': [50, 100],
+             'losses': ['logcosh', 'binary_crossentropy'],
+             'shapes': ['brick', 'triangle', 0.2],
+             'hidden_layers': [0, 1],
+             'activation': ['relu', 'elu'],
+             'last_activation': ['sigmoid']}
 
     return p
 

talos/templates/pipelines.py

@@ -44,7 +44,7 @@ def titanic(round_limit=2, random_method='uniform_mersenne', debug=False):
 
     '''Performs a Scan with Iris dataset and simple dense net'''
     import talos as ta
-    
+
     scan_object = ta.Scan(ta.templates.datasets.titanic()[0].astype('float32'),
                           ta.templates.datasets.titanic()[1].astype('float32'),
                           ta.templates.params.titanic(debug),

tests/commands/test_analyze.py

@@ -5,7 +5,6 @@ def test_analyze(scan_object):
     print('\n >>> Start Analyze()... \n')
 
     import talos
-    import glob
 
     # for now test with old name
     r = talos.Reporting(scan_object)
@@ -31,10 +30,6 @@ def test_analyze(scan_object):
     r.high('val_acc')
     r.low('val_acc')
 
-
-
-
-
     # r.plot_bars('first_neuron', 'val_acc', 'dropout', 'hidden_layers')
     r.plot_box('first_neuron', 'val_acc')
     r.plot_corr('val_loss', ['val_acc',

tests/commands/test_rest.py

@@ -21,12 +21,7 @@ def test_rest(scan_object):
     x_train, y_train, x_val, y_val = talos.utils.val_split(x, y, .2)
     x = talos.utils.rescale_meanzero(x)
 
-    import os
-    os.getcwd()
-    os.listdir()
-
-    callbacks = [
-                 talos.utils.early_stopper(10),
+    callbacks = [talos.utils.early_stopper(10),
                  talos.callbacks.ExperimentLog('test', {})]
 
     metrics = [talos.utils.metrics.f1score,

tests/commands/test_scan.py

@@ -5,7 +5,7 @@ def test_scan():
     import talos
 
     from tensorflow.keras.losses import binary_crossentropy
-    from tensorflow.keras.optimizers.legacy import Adam, Nadam
+    from tensorflow.keras.optimizers.legacy import Adam
     from tensorflow.keras.activations import relu, elu
     from tensorflow.keras.layers import Dense
     from tensorflow.keras.models import Sequential
@@ -49,12 +49,12 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
     x, y = talos.templates.datasets.iris()
 
-    p_for_q = {'activation':['relu', 'elu'],
+    p_for_q = {'activation': ['relu', 'elu'],
                'optimizer': ['Nadam', 'Adam'],
                'losses': ['logcosh'],
                'shapes': ['brick'],
                'first_neuron': [16, 32, 64, 128],
-               'hidden_layers':[0, 1, 2, 3],
+               'hidden_layers': [0, 1, 2, 3],
                'dropout': [.2, .3, .4],
                'batch_size': [20, 30, 40, 50],
                'epochs': [10]}
@@ -74,7 +74,6 @@ def iris_model(x_train, y_train, x_val, y_val, params):
                              reduction_metric='val_acc',
                              minimize_loss=False)
 
-
     x = x[:50]
     y = y[:50]
 
@@ -137,8 +136,8 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
     # the create the test based on it
 
-    _keras_model = scan_object.best_model()
-    _keras_model = scan_object.best_model('loss', True)
+    scan_object.best_model()
+    scan_object.best_model('loss', True)
 
     scan_object.evaluate_models(x_val=scan_object.x,
                                 y_val=scan_object.y,
@@ -155,7 +154,4 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
     print('finised Scan() object \n')
 
-    # # # # # # # # # # # # # # # # # #
-
-
     return scan_object