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Running on Zero
Running on Zero
| # visualizer.py | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| from sklearn.datasets import make_classification, make_regression, make_blobs | |
| from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, Lasso | |
| from sklearn.preprocessing import PolynomialFeatures | |
| from sklearn.pipeline import make_pipeline | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, IsolationForest | |
| from sklearn.svm import SVC | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.naive_bayes import GaussianNB | |
| from sklearn.cluster import KMeans, AgglomerativeClustering | |
| from sklearn.decomposition import PCA | |
| from sklearn.manifold import TSNE | |
| from sklearn.neural_network import MLPClassifier | |
| def _get_classifier(algo_adi, param): | |
| p = max(1, int(param)) | |
| if "Lojistik" in algo_adi: | |
| return LogisticRegression(C=float(param) * 0.5) | |
| elif "Karar Ağacı" in algo_adi: | |
| return DecisionTreeClassifier(max_depth=p, random_state=42) | |
| elif "Rastgele Orman" in algo_adi: | |
| return RandomForestClassifier(n_estimators=30, max_depth=p, random_state=42) | |
| elif "SVM" in algo_adi: | |
| return SVC(kernel='rbf', C=float(param) * 0.5, probability=True, random_state=42) | |
| elif "KNN" in algo_adi: | |
| return KNeighborsClassifier(n_neighbors=p) | |
| elif "Naive Bayes" in algo_adi: | |
| return GaussianNB(var_smoothing=10**(-p)) | |
| elif "Sinir Ağları" in algo_adi: | |
| return MLPClassifier(hidden_layer_sizes=(p * 8, p * 4), max_iter=200, random_state=42) | |
| else: | |
| return GradientBoostingClassifier(max_depth=min(6, p), n_estimators=40, random_state=42) | |
| def generate_ml_plot(detay, algo_adi, n_samples, noise, param): | |
| fig = go.Figure() | |
| n_samples = int(n_samples) | |
| noise = float(noise) | |
| param = float(param) | |
| if detay["tip"] == "Regresyon": | |
| X, y = make_regression(n_samples=n_samples, n_features=1, noise=noise * 25.0, random_state=42) | |
| x_raw = X.flatten() | |
| if "Ridge" in algo_adi: | |
| model = Ridge(alpha=param * 5.0) | |
| elif "Lasso" in algo_adi: | |
| model = Lasso(alpha=param * 2.0) | |
| else: | |
| deg = max(1, min(5, int(param))) | |
| model = make_pipeline(PolynomialFeatures(degree=deg), LinearRegression()) | |
| model.fit(X, y) | |
| x_sorted = np.linspace(x_raw.min(), x_raw.max(), 100).reshape(-1, 1) | |
| y_sorted = model.predict(x_sorted) | |
| fig.add_trace(go.Scatter( | |
| x=x_raw.tolist(), y=y.tolist(), mode='markers', name='Veri Noktaları', | |
| marker=dict(color='#818cf8', size=8, opacity=0.8) | |
| )) | |
| fig.add_trace(go.Scatter( | |
| x=x_sorted.flatten().tolist(), y=y_sorted.tolist(), mode='lines', name='Model Tahmini', | |
| line=dict(color='#ef4444', width=3) | |
| )) | |
| fig.update_layout( | |
| title=f"{algo_adi} Tahmin Eğrisi", | |
| xaxis=dict(title="Özellik (X)", autorange=True), | |
| yaxis=dict(title="Hedef (y)", autorange=True) | |
| ) | |
| elif detay["tip"] == "Kümeleme": | |
| k_val = max(2, int(param)) | |
| X, _ = make_blobs(n_samples=n_samples, centers=4, cluster_std=noise * 2 + 0.5, random_state=42) | |
| model = KMeans(n_clusters=k_val, random_state=42) if "K-Means" in algo_adi else AgglomerativeClustering(n_clusters=k_val) | |
| preds = model.fit_predict(X) | |
| fig.add_trace(go.Scatter( | |
| x=X[:, 0].tolist(), y=X[:, 1].tolist(), mode='markers', | |
| marker=dict(color=preds.tolist(), colorscale='Viridis', size=9, line=dict(width=1, color='white')), | |
| name="Kümeler" | |
| )) | |
| if "K-Means" in algo_adi: | |
| centers = model.cluster_centers_ | |
| fig.add_trace(go.Scatter( | |
| x=centers[:, 0].tolist(), y=centers[:, 1].tolist(), mode='markers', | |
| marker=dict(color='#ef4444', size=14, symbol='x'), | |
| name="Küme Merkezleri" | |
| )) | |
| fig.update_layout(title=f"{algo_adi} (K={k_val}) Kümeleme Sonucu") | |
| elif detay["tip"] == "Boyut İndirgeme": | |
| n_feat = max(4, int(param) + 3) | |
| X, y = make_classification(n_samples=n_samples, n_features=n_feat, n_informative=3, random_state=42) | |
| if "PCA" in algo_adi: | |
| X_2d = PCA(n_components=2).fit_transform(X) | |
| else: | |
| perp = min(40, max(5, int(param) * 4)) | |
| X_2d = TSNE(n_components=2, perplexity=min(perp, n_samples - 1), random_state=42).fit_transform(X) | |
| fig.add_trace(go.Scatter( | |
| x=X_2d[:, 0].tolist(), y=X_2d[:, 1].tolist(), mode='markers', | |
| marker=dict(color=y.tolist(), colorscale='Plasma', size=8), | |
| name="2D İzdüşüm" | |
| )) | |
| fig.update_layout(title=f"{algo_adi} 2 Boyutlu İzdüşümü", xaxis_title="Bileşen 1", yaxis_title="Bileşen 2") | |
| elif detay["tip"] == "Anomali Tespiti": | |
| contam = max(0.02, min(0.35, param / 25.0)) | |
| X, _ = make_blobs(n_samples=n_samples, centers=1, cluster_std=noise + 0.5, random_state=42) | |
| model = IsolationForest(contamination=contam, random_state=42) | |
| preds = model.fit_predict(X) | |
| colors = ['#ef4444' if p == -1 else '#818cf8' for p in preds] | |
| fig.add_trace(go.Scatter( | |
| x=X[:, 0].tolist(), y=X[:, 1].tolist(), mode='markers', | |
| marker=dict(color=colors, size=8), | |
| name="Veri Noktaları" | |
| )) | |
| fig.update_layout(title=f"Isolation Forest Anomali Tespiti (Oran: %{int(contam*100)})") | |
| else: | |
| X, y = make_classification( | |
| n_samples=n_samples, n_features=2, n_redundant=0, | |
| n_informative=2, n_clusters_per_class=1, flip_y=noise * 0.35, random_state=42 | |
| ) | |
| clf = _get_classifier(algo_adi, param) | |
| clf.fit(X, y) | |
| acc = clf.score(X, y) | |
| x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 | |
| y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 | |
| xx, yy = np.meshgrid(np.linspace(x_min, x_max, 40), np.linspace(y_min, y_max, 40)) | |
| if hasattr(clf, "predict_proba"): | |
| Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1] | |
| else: | |
| Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) | |
| Z = Z.reshape(xx.shape) | |
| fig.add_trace(go.Contour( | |
| x=np.linspace(x_min, x_max, 40).tolist(), | |
| y=np.linspace(y_min, y_max, 40).tolist(), | |
| z=Z.tolist(), | |
| colorscale=[[0, 'rgba(99, 102, 241, 0.25)'], [1, 'rgba(52, 211, 153, 0.25)']], | |
| showscale=False, | |
| contours=dict(coloring='fill', showlines=True), | |
| name="Karar Sınırı" | |
| )) | |
| colors = ['#6366f1' if label == 0 else '#10b981' for label in y] | |
| fig.add_trace(go.Scatter( | |
| x=X[:, 0].tolist(), y=X[:, 1].tolist(), mode='markers', | |
| marker=dict(color=colors, size=8, line=dict(width=1, color='#1e1e2e')), | |
| name="Sınıflar" | |
| )) | |
| fig.update_layout( | |
| title=f"{algo_adi} Karar Sınırı (Doğruluk: %{acc*100:.1f})", | |
| xaxis_title="Özellik 1", yaxis_title="Özellik 2" | |
| ) | |
| fig.update_layout( | |
| template="plotly_dark", | |
| margin=dict(l=20, r=20, t=45, b=20), | |
| legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) | |
| ) | |
| return fig |