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modules/machine_learning/logistic_regression.py

@@ -4,52 +4,64 @@ import numpy as np
 import scipy.optimize as opt
 from sklearn import preprocessing
 import matplotlib.pyplot as plt
-from sklearn.metrics import confusion_matrix
+from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, jaccard_score, f1_score, precision_score, recall_score, roc_auc_score
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 
-icing_df = pd.read_csv('/Users/tomrink/train_L2_DAY_1D.csv')
-#print(icing_df.head(20))
-#print(icing_df.describe())
-print(icing_df.shape)
-
-# Remove rows with NaN values
-# icing_df = icing_df.dropna()
-
-#Access rows
-print(icing_df.iloc[0]) #First row of DataFrame
-print('--------------------------------------')
-print(icing_df.iloc[200]) #Eleventh row of DataFrame
-
-#Access columns
-print(icing_df['lwc_dcomp']) #Replace COLUMN_NAME with the name of column
-
-#Remove column
-icing_df = icing_df.drop('lwc_dcomp', axis=1)
-
-print(icing_df.shape)
-# icing_df = icing_df.dropna()
-print(icing_df.shape)
-
-params = ['cld_geo_thick', 'cld_temp_acha', 'conv_cloud_fraction', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp']
-X = np.asarray(icing_df[params])
-# X = preprocessing.StandardScaler().fit(X).transform(X)
-y = np.asarray(icing_df['icing_intensity'])
-y = np.where(y == -1, 0, y)
-print(X.shape, y.shape)
-print('num no icing: ', np.sum(y == 0))
-print('num icing: ', np.sum(y == 1))
-
-X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)
-print ('Train set:', X_train.shape,  y_train.shape)
-print ('Test set:', X_test.shape,  y_test.shape)
-X_train = np.where(np.isnan(X_train), 0, X_train)
-X_test = np.where(np.isnan(X_test), 0, X_test)
-print('num no icing test: ', np.sum(y_test == 0))
-print('num icing test: ', np.sum(y_test == 1))
-
-
-LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)
-yhat = LR.predict(X_test)
-yhat_prob = LR.predict_proba(X_test)
-print(confusion_matrix(y_test, yhat, labels=[1,0]))
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+
+def get_csv_as_dataframe(csv_file):
+    icing_df = pd.read_csv(csv_file)
+    print(icing_df.describe())
+    print(icing_df.shape)
+    return icing_df
+
+
+def get_train_test_data(data_frame, normalize=True):
+    icing_df = data_frame
+    # The independent variables we want to use:
+    params = ['cld_geo_thick', 'cld_temp_acha', 'conv_cloud_fraction', 'supercooled_cloud_fraction', 'cld_reff_dcomp',
+              'cld_opd_dcomp', 'iwc_dcomp']
+    # Remove this column
+    icing_df = icing_df.drop('lwc_dcomp', axis=1)
+
+    # Remove rows with NaN values
+    # icing_df = icing_df.dropna()
+
+    print(icing_df.shape)
+    # icing_df = icing_df.dropna()
+    print(icing_df.shape)
+
+    x = np.asarray(icing_df[params])
+    if normalize:
+        x = preprocessing.StandardScaler().fit(x).transform(x)
+    y = np.asarray(icing_df['icing_intensity'])
+    y = np.where(y == -1, 0, y)
+    y = np.where(y >= 1, 1, y)
+    print(x.shape, y.shape)
+    print('num no icing: ', np.sum(y == 0))
+    print('num icing: ', np.sum(y == 1))
+
+    return x, y
+
+
+def logistic_regression(x, y):
+    x_train, x_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=4)
+    print('Train set:', x_train.shape,  y_train.shape)
+    print('Test set:', x_test.shape,  y_test.shape)
+
+    x_train = np.where(np.isnan(x_train), 0, x_train)
+    x_test = np.where(np.isnan(x_test), 0, x_test)
+    print('num no icing test: ', np.sum(y_test == 0))
+    print('num icing test: ', np.sum(y_test == 1))
+
+    LR = LogisticRegression(C=0.01, solver='liblinear').fit(x_train, y_train)
+    yhat = LR.predict(x_test)
+    yhat_prob = LR.predict_proba(x_test)
+
+    print(confusion_matrix(y_test, yhat, labels=[1,0]))
+    print('Accuracy:    ', accuracy_score(y_test, yhat))
+    print('Jaccard Idx: ', jaccard_score(y_test, yhat))
+    print('Precision:   ', precision_score(y_test, yhat))
+    print('Recall:      ', recall_score(y_test, yhat))
+    print('F1:          ', f1_score(y_test, yhat))
+    print('AUC:         ', roc_auc_score(y_test, yhat_prob[:, 1]))
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