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src.visualisations.common – Shared chart builders (used by both classification and regression).
"""
from __future__ import annotations
from typing import Dict, List, Optional
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
from src import config
_PLOTLY_TEMPLATE = "plotly_dark"
_FONT_FAMILY = "Inter, sans-serif"
def _base_layout(**kwargs) -> dict:
return dict(
template=_PLOTLY_TEMPLATE,
font=dict(family=_FONT_FAMILY, size=13),
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
**kwargs,
)
def correlation_heatmap(data: pd.DataFrame) -> plt.Figure:
"""Return a Matplotlib figure with a styled correlation heatmap."""
fig, ax = plt.subplots(figsize=(max(8, data.shape[1]), max(6, data.shape[1] - 1)))
fig.patch.set_facecolor("#0E1117")
ax.set_facecolor("#0E1117")
mask = np.triu(np.ones_like(data.corr(), dtype=bool))
sns.heatmap(
data.corr(), mask=mask, annot=True, fmt=".2f", cmap="coolwarm",
center=0, ax=ax, annot_kws={"size": 9, "color": "white"},
linewidths=0.5, linecolor="#1E2130",
)
ax.tick_params(colors="white")
for spine in ax.spines.values():
spine.set_edgecolor("#1E2130")
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", color="white")
plt.setp(ax.get_yticklabels(), color="white")
ax.set_title("Feature Correlation Matrix", color="white", fontsize=14, pad=12)
plt.tight_layout()
return fig
def feature_distributions(data: pd.DataFrame, target_col: Optional[str] = None) -> go.Figure:
"""Interactive histograms for all numeric columns."""
num_cols = data.select_dtypes(include="number").columns.tolist()
if target_col and target_col in num_cols:
num_cols = [c for c in num_cols if c != target_col]
n_cols = 3
n_rows = max(1, (len(num_cols) + n_cols - 1) // n_cols)
from plotly.subplots import make_subplots
fig = make_subplots(rows=n_rows, cols=n_cols, subplot_titles=num_cols)
for i, col in enumerate(num_cols):
row, col_idx = divmod(i, n_cols)
trace = go.Histogram(
x=data[col], name=col,
marker_color=config.COLOR_PALETTE[i % len(config.COLOR_PALETTE)],
opacity=0.85,
)
fig.add_trace(trace, row=row + 1, col=col_idx + 1)
fig.update_layout(
**_base_layout(title_text="Feature Distributions", showlegend=False),
height=n_rows * 250,
)
return fig
def model_comparison_chart(results_df: pd.DataFrame) -> go.Figure:
"""Grouped bar chart comparing all metrics across models."""
df = results_df.reset_index() if "Model" not in results_df.columns else results_df.copy()
if "Model" not in df.columns and df.index.name == "Model":
df = df.reset_index()
metric_cols = [c for c in df.columns if c not in ("Model", "CV Score", "index")]
melted = df.melt(id_vars="Model", value_vars=metric_cols, var_name="Metric", value_name="Score")
fig = px.bar(
melted.dropna(subset=["Score"]),
x="Model", y="Score", color="Metric", barmode="group",
color_discrete_sequence=config.COLOR_PALETTE,
title="📊 Model Performance Comparison",
labels={"Score": "Score", "Model": ""},
)
fig.update_layout(
**_base_layout(legend_title_text="Metric"),
xaxis=dict(gridcolor="#2A2E3F"),
)
return fig
def feature_importance_chart(feature_names: List[str], importances: np.ndarray) -> go.Figure:
"""Horizontal bar chart of feature importances."""
df = pd.DataFrame({"Feature": feature_names, "Importance": importances}).sort_values("Importance", ascending=True)
fig = px.bar(
df, x="Importance", y="Feature", orientation="h",
color="Importance", color_continuous_scale=["#6C63FF", "#FF6584"],
title="🌟 Feature Importances",
)
fig.update_layout(
**_base_layout(coloraxis_showscale=False),
xaxis=dict(gridcolor="#2A2E3F"),
yaxis=dict(gridcolor="#2A2E3F"),
)
return fig
def target_distribution_chart(y: pd.Series, label_encoders: dict, target_col: str) -> go.Figure:
"""Pie chart of target class distribution."""
counts = y.value_counts().reset_index()
counts.columns = ["Class", "Count"]
if target_col in label_encoders:
le = label_encoders[target_col]
counts["Class"] = le.inverse_transform(counts["Class"].astype(int))
fig = px.pie(
counts, names="Class", values="Count",
color_discrete_sequence=config.COLOR_PALETTE,
title="🎯 Target Class Distribution", hole=0.4,
)
fig.update_layout(**_base_layout())
return fig
def target_histogram_chart(y: pd.Series, target_col: str) -> go.Figure:
"""Histogram of a continuous target variable."""
fig = px.histogram(
x=y, nbins=40, color_discrete_sequence=["#6C63FF"],
title=f"🎯 Target Distribution — {target_col}",
labels={"x": target_col, "y": "Count"},
)
fig.update_layout(
**_base_layout(showlegend=False),
xaxis=dict(gridcolor="#2A2E3F"),
yaxis=dict(gridcolor="#2A2E3F"),
)
return fig
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