155 lines
4.9 KiB
Python
155 lines
4.9 KiB
Python
import pandas as pd
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.metrics import f1_score
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.preprocessing import LabelEncoder
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# low amounts of features will result in many zero devision in tp=0 and fp=0
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np.seterr(divide='ignore', invalid='ignore')
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FEATURES = ["points", "x", "y"]
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# create dataframe with csv file
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def make_dataframe(transform):
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def load_dataframe(file_path):
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try:
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colum_list = FEATURES
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df = pd.read_csv(file_path, usecols = colum_list).dropna()
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return transform(df)
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except FileNotFoundError as error:
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print(error)
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quit()
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return load_dataframe
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# depending on mode, [x, y] cordinates are used as feature or length of vector (x, y) [radius] is used
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def make_features(selector):
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def select(df):
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return df
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return select(selector)
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# Feature radius when mode = v
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def radius(df):
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df["radius"] = np.sqrt(df["x"]**2 + df["y"]**2)
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return df[["radius"]]
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# Feature ["x", "y"] when mode = a or c
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def xy(df):
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features = ["x", "y"]
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return df[features]
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# apply model on dataframe. Params: df = dataframe, features = function make_features, inf = True or False, graph = True or False
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def apply_model(df, features, score, inf, graph):
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# print dataframe information
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if inf:
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print(df.describe())
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print(df.head())
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print(df.head().info())
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# display graphs
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if graph:
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sns.countplot(x = df["points"])
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plt.show()
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sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
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plt.show()
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sns.scatterplot(x=df['x'], y=df['y'], hue=df['points'])
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plt.show()
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y = pd.get_dummies(df['points'])
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X = features(df) # select which features to use radius or xy
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# Split data into 60/40 (train/test)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
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# Create a RandomForestClassifier with n_estimators=700
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random_forest = RandomForestClassifier(n_estimators=700, random_state=0)
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# Create a DecisionTreeClassifier
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decision_tree = DecisionTreeClassifier(random_state=0)
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# Create a KNeighborsClassifier with n_neighbors=5
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k_neighbors = KNeighborsClassifier(n_neighbors=5)
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models = {
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"Random Forest Classifier": random_forest,
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"Decision Tree Classifier": decision_tree,
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"K-Neighbors": k_neighbors
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}
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for name, model in models.items():
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model.fit(X_train.values, y_train.values)
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for name, model in models.items():
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pred = model.predict(X_test.values)
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# calculate f1 with own function
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my_f1_macro_score = calc_f1_macro(y_test, pd.DataFrame(pred))
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print(f'My F1 score of {name} is {my_f1_macro_score}')
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# calculate f1 with sklearn function
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f1_sklearn = f1_score(y_test.values, pred, average='macro')
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print(f'Sklearn F1 score of {name} is {f1_sklearn}')
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score = score() # promt for x, y coordinates and transform score based on mode
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label_encoder = LabelEncoder()
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df["points"] = label_encoder.fit_transform(df["points"])
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for name, model in models.items():
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pred = model.predict(score)
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points_number = pd.DataFrame(pred).idxmax(axis=1)
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points = label_encoder.inverse_transform(points_number)[0]
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print(f"{name}: {points} Punkte")
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input("\nPress any key to continue...\n")
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# calc f1 macro
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def calc_f1_macro(y_true, y_pred):
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f1_scores = []
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for column in y_true:
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score = calc_f1_score(y_true[column].values, y_pred[column])
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f1_scores.append(score)
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return np.mean(f1_scores)
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# calc f1 score
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def calc_f1_score(y_true, y_pred):
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tp = np.sum(np.multiply([i==True for i in y_pred], y_true))
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tn = np.sum(np.multiply([i==False for i in y_pred], [not(j) for j in y_true]))
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fp = np.sum(np.multiply([i==True for i in y_pred], [not(j) for j in y_true]))
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fn = np.sum(np.multiply([i==False for i in y_pred], y_true))
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precision = calc_precision(tp, fp)
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recall = calc_recall(tp, fn)
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if precision != 0 and recall != 0:
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f1 = (2 * precision * recall) / (precision + recall)
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else:
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f1 = 0
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return f1
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# calc precision
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def calc_precision(tp, fp):
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return tp / (tp + fp)
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# calc recall
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def calc_recall(tp, fn):
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return tp / (tp + fn)
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# ask for x, y value and return transformed array based on mode
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def make_score_function(transform):
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def get_score_from_cli():
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try:
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x = float(input("x: "))
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y = float(input("y: "))
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return np.array([transform(x, y)]).reshape(1, -1)
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except ValueError:
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print("Invalid input. Please enter numeric values.")
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return None
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return get_score_from_cli |