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88 lines
2.5 KiB
88 lines
2.5 KiB
#!/usr/bin/env python3 |
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import pandas as pd |
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import numpy as np |
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import datetime |
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import sys |
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from sklearn.model_selection import train_test_split |
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from sklearn.metrics import roc_auc_score |
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from sklearn.metrics import accuracy_score |
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from sklearn.metrics import precision_score |
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from sklearn.metrics import recall_score |
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from sklearn.metrics import f1_score |
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from sklearn.metrics import confusion_matrix |
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from sklearn.neighbors import KNeighborsClassifier |
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from sklearn.linear_model import * |
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from sklearn.ensemble import RandomForestClassifier |
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from sklearn.ensemble import GradientBoostingClassifier |
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from sklearn.preprocessing import StandardScaler |
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from sklearn.svm import SVC |
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from sklearn.neural_network import MLPClassifier |
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# load train data |
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data=pd.read_csv('traindata_sea.csv') |
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# add feature colomn |
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data['flash'] = np.heaviside(data['lightning_count']-1,0) |
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# select target colomn |
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wwlln=data['flash'] |
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# del all but sattelite data |
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del data['flash'] |
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del data['lat'] |
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del data['lon'] |
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del data['ptime'] |
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del data['lightning_count'] |
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del data['avg_energy'] |
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print(data.head()) |
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folds=5 # how many folds |
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scaler = StandardScaler() |
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# select classificator |
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clf = LogisticRegression(penalty='l2', class_weight={1: 0.774}) |
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#clf = RandomForestClassifier(n_estimators=50) |
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#clf = MLPClassifier(hidden_layer_sizes=(5,), shuffle=True,verbose=False) |
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accur, recall, f1 = 0, 0 ,0 |
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matrix = np.zeros((4)) |
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start_time = datetime.datetime.now() |
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coefs = np.empty([1,data.shape[1]]) |
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for fold in range(folds): |
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print('N_fold is:', fold) |
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X_train, X_test, y_train, y_test = train_test_split(data, wwlln, test_size=1/folds, shuffle=True) |
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X_train_scaled = scaler.fit_transform(X_train) |
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X_test_scaled = scaler.transform(X_test) |
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clf.fit(X_train_scaled, y_train) |
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try: |
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coefs += clf.coef_ |
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except: |
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print('There are NO coeffs for this clf!') |
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predict = clf.predict(X_test_scaled) |
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accur += precision_score(y_test, predict) |
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recall += recall_score(y_test, predict) |
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f1 += f1_score(y_test, predict) |
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matrix += confusion_matrix(y_test, predict).ravel() |
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print (f1) |
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time = datetime.datetime.now() - start_time |
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print (pd.DataFrame((coefs/folds)/np.max(np.abs(coefs/folds)))) |
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print ('accur', accur/folds) |
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print ('recall', recall/folds) |
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print ('f1', f1/folds) |
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print ('TN', 'FP', 'FN', 'TP') |
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print (matrix) |
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print('Total events:', matrix.sum()) |
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print('By set:', 105295) |
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if matrix.sum() == 105295: |
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print('Match! OK!') |
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print ('time', time)
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