154 lines
5.0 KiB
Python
154 lines
5.0 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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FEATURES = ['Flipper Length (mm)','Body Mass (g)','Culmen Depth (mm)','Culmen Length (mm)', 'Species']
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def load_dataframe():
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try:
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column_list = FEATURES
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df = pd.read_csv("penguins.csv", usecols = column_list)
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return df
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except FileNotFoundError:
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print("Datei 'penguins.csv' nicht gefunden.")
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return None
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def calc_precision(tp, fp):
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return tp / (tp + fp)
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def calc_recall(tp, fn):
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return tp / (tp + fn)
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def calc_f1_score(y_true, y_pred):
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#https://stackoverflow.com/questions/64860091/computing-macro-average-f1-score-using-numpy-pythonwithout-using-scikit-learn
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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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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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def get_penguin_from_cli():
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try:
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culmen_depth = float(input("Culmen Depth (mm): "))
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culmen_length = float(input("Culmen Length (mm): "))
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return np.array([culmen_depth, culmen_length]).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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def main():
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df = load_dataframe()
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if df is None:
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return
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print("\n=== Overview ===")
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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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print("\n=== Quality Assessment ===")
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row_count = len(df)
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print("Number of rows:", row_count)
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# check min, max, mean ...
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# See df.describe() above
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print("Check for null-values:", df.isnull().sum())
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print("\n=== Preprocessing ===")
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# fill null-values with mean
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df.fillna(df.mean(numeric_only=True), inplace=True)
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# transform species column to numbers
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labelencoder = LabelEncoder()
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df['Species'] = labelencoder.fit_transform(df['Species'])
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print("\n=== Countplot ===")
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# Countplot check for the balancing of the data
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sns.countplot(x = df['Species'])
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plt.show()
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print("\n=== Heatmap ===")
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# Check correlation among other variables
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sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
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plt.show()
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print("\n=== Feature Selection ===")
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features = ['Culmen Depth (mm)', 'Culmen Length (mm)']
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y = df['Species']
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X = df[features]
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y = pd.get_dummies(y)
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print("\n=== Visualize Features ===")
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sns.scatterplot(x=df['Culmen Length (mm)'], y=df['Culmen Depth (mm)'], hue=df['Species'])
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plt.show()
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print("\n=== Model Training ===")
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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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print("\n=== Model Evaluation ===")
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for name, model in models.items():
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pred = model.predict(X_test.values)
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# Hint: calc_f1_macro expects "pred" to be a DataFrame --> pd.DataFrame(pred)
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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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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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print("\n=== Prediction ===")
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# Culmen Depth (mm) = 18, Culmen Length (mm) = 50
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#wild_penguin = np.array([18, 50]).reshape(1, -1)
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wild_penguin = get_penguin_from_cli()
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for name, model in models.items():
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pred = model.predict(wild_penguin)
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species_number = pd.DataFrame(pred).idxmax(axis=1)
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species = labelencoder.inverse_transform(species_number)[0]
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print(f'{name}: Dieser Pinguin gehört der Spezies "{species}" an')
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if __name__ == "__main__":
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main()
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