Hi All, I have the below code. X = df.iloc[:, [4, 403]].values y = df.iloc[:, 404].values
Dummy Data looks like: host Mnemonic 12.234.13.6 start 22.22.44.67 something 23.44.44.14 begin When I define the X and Y values for prediction in the train and test data, should I capture all the columns that has been "OneHotEncoded" (that is all columns with 0 and 1) for the X and Y values??? import numpy as np import pandas as pd import os import matplotlib as mpl mpl.rcParams['figure.dpi'] = 400 import matplotlib.pyplot as plt # Importing the df # Importing the df os.chdir('c:\directory\data') # Location of data files df = pd.read_csv('blahblahfile.csv') from sklearn.preprocessing import LabelEncoder hostip = LabelEncoder() mnemonic = LabelEncoder() df['host_encoded'] = hostip.fit_transform(df.reported_hostname) df['mnemonic_encoded'] = mnemonic.fit_transform(df.mnemonic) from sklearn.preprocessing import OneHotEncoder hostip_ohe = OneHotEncoder() mnemonic_ohe = OneHotEncoder() X = hostip_ohe.fit_transform(df.host_encoded.values.reshape(-1,1)).toarray() Y = mnemonic_ohe.fit_transform(df.mnemonic_encoded.values.reshape(-1,1)).toarray() ## Add back X and Y into the original dataframe dfOneHot = pd.DataFrame(X, columns = ["host_"+str(int(i)) for i in range(X.shape[1])]) df = pd.concat([df, dfOneHot], axis=1) dfOneHot = pd.DataFrame(Y, columns = ["mnemonic_encoded"+str(int(i)) for i in range(Y.shape[1])]) df = pd.concat([df, dfOneHot], axis=1) ######## here is where I am not sure if all "host_" and "mnemonic_encoded" values assigned to X and Y X = df.iloc[:, [4, 403]].values y = df.iloc[:, 404].values # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # Fitting Logistic Regression to the Training set from sklearn.linear_model import LogisticRegression classifier = LogisticRegression(random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) # Visualising the Training set results from matplotlib.colors import ListedColormap X_set, y_set = X_train, y_train X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(), X1.max()) plt.ylim(X2.min(), X2.max()) for i, j in enumerate(np.unique(y_set)): plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j) plt.title('Logistic Regression (Training set)') plt.xlabel('Age') plt.ylabel('Estimated Salary') plt.legend() plt.show() # Visualising the Test set results from matplotlib.colors import ListedColormap X_set, y_set = X_test, y_test X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01), np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01)) plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(), X1.max()) plt.ylim(X2.min(), X2.max()) for i, j in enumerate(np.unique(y_set)): plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('red', 'green'))(i), label = j) plt.title('Logistic Regression (Test set)') plt.xlabel('Host IP') plt.ylabel('Mnemonic') plt.legend() plt.show() -- https://mail.python.org/mailman/listinfo/python-list