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f3a6f24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.metrics import roc_curve, auc, confusion_matrix
def plot_churn_distribution(df: pd.DataFrame) -> go.Figure:
counts = df["Churn"].value_counts().reset_index()
counts.columns = ["Churn", "Count"]
counts["Label"] = counts["Churn"].map({0: "Retained", 1: "Churned"})
fig = px.pie(
counts, values="Count", names="Label", hole=0.45,
color="Label",
color_discrete_map={"Retained": "#636EFA", "Churned": "#EF553B"},
)
fig.update_traces(textinfo="percent+label+value")
fig.update_layout(title="Customer Churn Distribution", showlegend=False)
return fig
def plot_feature_histogram(df: pd.DataFrame, feature: str) -> go.Figure:
temp = df.copy()
temp["Churn_Label"] = temp["Churn"].map({0: "Retained", 1: "Churned"})
fig = px.histogram(
temp, x=feature, color="Churn_Label", barmode="overlay",
color_discrete_map={"Retained": "#636EFA", "Churned": "#EF553B"},
opacity=0.7,
)
fig.update_layout(title=f"Distribution of {feature} by Churn Status")
return fig
def plot_categorical_churn_rate(df: pd.DataFrame, feature: str) -> go.Figure:
grouped = df.groupby(feature)["Churn"].mean().reset_index()
grouped.columns = [feature, "Churn Rate"]
grouped["Churn Rate"] = (grouped["Churn Rate"] * 100).round(1)
fig = px.bar(
grouped, x=feature, y="Churn Rate",
text="Churn Rate", color="Churn Rate",
color_continuous_scale="RdYlGn_r",
)
fig.update_traces(texttemplate="%{text:.1f}%", textposition="outside")
fig.update_layout(
title=f"Churn Rate by {feature}",
yaxis_title="Churn Rate (%)",
coloraxis_showscale=False,
)
return fig
def plot_correlation_heatmap(df: pd.DataFrame, numeric_cols: list[str]) -> go.Figure:
corr = df[numeric_cols].corr()
fig = px.imshow(
corr, text_auto=".2f", color_continuous_scale="RdBu_r",
aspect="auto", zmin=-1, zmax=1,
)
fig.update_layout(title="Feature Correlation Heatmap")
return fig
def plot_roc_curves(model_entries: list, y_test) -> go.Figure:
"""Accept list of (name, model, X_test) tuples so each model can use its own test data."""
fig = go.Figure()
for name, model, X_test in model_entries:
if hasattr(model, "predict_proba"):
y_proba = model.predict_proba(X_test)[:, 1]
else:
y_proba = model.decision_function(X_test)
fpr, tpr, _ = roc_curve(y_test, y_proba)
roc_auc = auc(fpr, tpr)
fig.add_trace(go.Scatter(
x=fpr, y=tpr, mode="lines",
name=f"{name} (AUC={roc_auc:.3f})",
))
fig.add_trace(go.Scatter(
x=[0, 1], y=[0, 1], mode="lines",
line=dict(dash="dash", color="gray"), name="Random",
))
fig.update_layout(
title="ROC Curves — Model Comparison",
xaxis_title="False Positive Rate",
yaxis_title="True Positive Rate",
legend=dict(x=0.55, y=0.05),
)
return fig
def plot_confusion_matrix(y_true, y_pred, title: str = "Confusion Matrix") -> go.Figure:
cm = confusion_matrix(y_true, y_pred)
labels = ["Retained", "Churned"]
fig = px.imshow(
cm, text_auto=True, color_continuous_scale="Blues",
x=labels, y=labels, aspect="equal",
)
fig.update_layout(
title=title,
xaxis_title="Predicted",
yaxis_title="Actual",
coloraxis_showscale=False,
)
return fig
def plot_gauge(probability: float) -> go.Figure:
color = "#2ecc71" if probability < 0.3 else "#f39c12" if probability < 0.6 else "#e74c3c"
fig = go.Figure(go.Indicator(
mode="gauge+number",
value=probability * 100,
number={"suffix": "%"},
gauge={
"axis": {"range": [0, 100]},
"bar": {"color": color},
"steps": [
{"range": [0, 30], "color": "#d5f5e3"},
{"range": [30, 60], "color": "#fdebd0"},
{"range": [60, 100], "color": "#fadbd8"},
],
},
title={"text": "Churn Probability"},
))
fig.update_layout(height=300)
return fig
def plot_segments(X_2d: np.ndarray, cluster_labels: np.ndarray, churn: np.ndarray) -> go.Figure:
seg_df = pd.DataFrame({
"UMAP_1": X_2d[:, 0],
"UMAP_2": X_2d[:, 1],
"Cluster": cluster_labels.astype(str),
"Churn": np.where(churn == 1, "Churned", "Retained"),
})
fig = px.scatter(
seg_df, x="UMAP_1", y="UMAP_2",
color="Cluster", symbol="Churn",
opacity=0.6,
symbol_map={"Churned": "x", "Retained": "circle"},
)
fig.update_layout(
title="Customer Segments (UMAP Projection)",
xaxis_title="UMAP Dimension 1",
yaxis_title="UMAP Dimension 2",
)
return fig
def plot_metric_history(history: dict[str, list[float]], batch_labels: list[str]) -> go.Figure:
fig = go.Figure()
for metric_name, values in history.items():
fig.add_trace(go.Scatter(
x=batch_labels[:len(values)], y=values,
mode="lines+markers", name=metric_name,
))
fig.update_layout(
title="Model Performance Over Streaming Batches",
xaxis_title="Batch",
yaxis_title="Score",
yaxis=dict(range=[0, 1]),
)
return fig
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