Кодировки GAF и MTF окон доходности ETF

# 08_cnn_image_encoding.py


```py
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# %% [markdown]
# # Turning a window into a picture: GAF and MTF encodings
#
# **Docker image**: `ml4t-gpu`
#
# Every architecture in this section so far has kept the window as a sequence and
# argued about how to relate its positions - attention, dilated convolution, mixing,
# a recurrent state. This notebook takes a different route: it turns each window into
# a two-channel image and hands it to an ordinary image CNN, so that the whole
# apparatus built for pictures applies unchanged.
#
# The encodings are the point. A **Gramian angular field** rescales the window to
# $[-1, 1]$, reads each value as an angle, and fills cell $(i, j)$ with a function of
# the two angles - so the image is a table of every pair of positions, and a
# convolution over it reads relations between pairs rather than between values. A
# **Markov transition field** bins the window into quantiles and fills cell $(i, j)$
# with how often the series moved between the bins those two positions fall in.
#
# What the transformation discards is as important as what it builds, and both
# encodings below discard the same thing: they normalise inside each window, so the
# level and the range of the returns are gone before the CNN sees anything. Two
# windows of the same shape and different volatility become the same picture.
#
# **Learning objectives**:
# - Build both encodings from the definition and say what each cell of the resulting
#   matrix means.
# - Say what the per-window normalisation throws away, and why that matters for a
#   label whose scale is a return.
# - Read the resampling step: a `LOOKBACK`-day window becomes an
#   `IMAGE_SIZE`-by-`IMAGE_SIZE` image, so check whether that interpolates up or
#   summarises down before treating pixels as data.
# - Score the CNN against a penalised linear map on the same flattened pixels, so the
#   comparison isolates the CNN and not the encoding.
#
# **Book Reference**: Chapter 13, Section 13.6 (Alternative architectures and foundation models)
#
# **Prerequisites**: ETF features (`case_studies/etfs/`)

# %%
"""CNN with Time Series Image Encoding - convert time series to GAF/MTF images for forward-return regression."""

import os

os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8")

import matplotlib.pyplot as plt
import numpy as np
import plotly.graph_objects as go
import polars as pl
import torch
import torch.nn as nn
from dl_sequences import create_sequences_multi_asset, load_dl_dataset, train_model
from ml4t.diagnostic.metrics import cross_sectional_ic_series
from plotly.subplots import make_subplots
from sklearn.decomposition import PCA
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler

from utils.reproducibility import set_global_seeds
from utils.style import COLORS, add_message_title, show_plotly_with_alt, show_with_alt

# %% tags=["parameters"]
SEED = 42
LOOKBACK = 20
IMAGE_SIZE = 32
EPOCHS = 10
BATCH_SIZE = 64
LR = 1e-4
DROPOUT = 0.5
MAX_TRAIN_SAMPLES = 40_000
MAX_VAL_SAMPLES = 10_000
MAX_TEST_SAMPLES = 10_000
INFER_BATCH_SIZE = 1_024
LABEL_HORIZON = 21

# %%

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {DEVICE}")

set_global_seeds(SEED)
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True

# %% [markdown]
# ## Data Loading
#
# We use the five-day ETF return from the case-study pipeline. Both models see
# exactly the same one-feature windows.

# %%
mds = load_dl_dataset("etfs")

FEATURE_COLS = ["ret_5d"]
TARGET_COL = mds.label_col

missing_features = sorted(set(FEATURE_COLS) - set(mds.feature_names))
if missing_features:
    raise ValueError(f"Missing required ETF return features: {missing_features}")

print(f"Features ({len(FEATURE_COLS)}): {FEATURE_COLS}")
print(f"Target: {TARGET_COL}")

# %% [markdown]
# ## Sequence Creation and Temporal Split

# %%
df = mds.dataset.drop_nulls(subset=FEATURE_COLS + [TARGET_COL])
print(f"Rows after dropping nulls: {len(df):,}")
per_date = df.group_by(mds.date_col).len().sort(mds.date_col)
print(
    f"{df[mds.date_col].min()} to {df[mds.date_col].max()}, "
    f"{df[mds.entity_cols[0]].n_unique()} funds; funds per date "
    f"{per_date['len'].min()} to {per_date['len'].max()}, median {per_date['len'].median():.0f}"
)
print(
    f"Label {TARGET_COL}: mean {df[TARGET_COL].mean():+.5f}, "
    f"standard deviation {df[TARGET_COL].std():.5f}"
)

X, y, timestamps, symbols = create_sequences_multi_asset(
    df,
    FEATURE_COLS,
    TARGET_COL,
    LOOKBACK,
    timestamp_col=mds.date_col,
    symbol_col=mds.entity_cols[0],
)
print(f"Sequences: {X.shape[0]:,}, shape: {X.shape}")

sequence_order = np.lexsort((symbols.astype(str), timestamps))
X = np.nan_to_num(X[sequence_order], nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32)
y = np.nan_to_num(y[sequence_order], nan=0.0).astype(np.float32)
timestamps = timestamps[sequence_order]
symbols = symbols[sequence_order]

# %% [markdown]
# ### Splitting by date, with a gap for the label horizon
#
# The split is by date, at fixed fractions of the trading days, and an example belongs
# to the partition the date it carries falls in. The label is a `LABEL_HORIZON`-day
# forward return, so an example dated within that many days of a boundary has an
# outcome resolved by days on the far side; those examples are dropped. Input windows
# may still reach back over a boundary, which is right - at decision time the model has
# every past observation available.

# %%
unique_dates = np.sort(np.unique(timestamps))
train_boundary_idx = int(len(unique_dates) * 0.6)
val_boundary_idx = int(len(unique_dates) * 0.8)
train_end_date = unique_dates[train_boundary_idx]
val_end_date = unique_dates[val_boundary_idx]
train_label_cutoff = unique_dates[train_boundary_idx - LABEL_HORIZON]
val_label_cutoff = unique_dates[val_boundary_idx - LABEL_HORIZON]

train_mask = timestamps < train_label_cutoff
val_mask = (timestamps >= train_end_date) & (timestamps < val_label_cutoff)
test_mask = timestamps >= val_end_date

X_train, y_train = X[train_mask], y[train_mask]
X_val, y_val = X[val_mask], y[val_mask]
X_test, y_test = X[test_mask], y[test_mask]
test_dates, test_symbols = timestamps[test_mask], symbols[test_mask]

print(f"Train: {len(X_train):,}, Val: {len(X_val):,}, Test: {len(X_test):,}")
print(
    f"Purged {LABEL_HORIZON} target dates before each boundary before complete-date subsampling: "
    f"validation starts {train_end_date}, test starts {val_end_date}"
)


# %% [markdown]
# ### Pedagogical subsampling
#
# The GAF/MTF encoding loop produces one (image_size x image_size x 2) tensor
# per sample, so the full ETF panel takes considerable wall-clock to encode.
# We cap each split at a few tens of thousands of sequences for tractable
# runtime. Critically we subsample by **complete dates**, not row count: the
# sequence array can begin or end partway through a date, so a raw row slice
# would leave a partial cross-section. Keeping the most recent complete dates
# instead gives a stable, full cross-section that reproduces across runs.


# %%
def _trim_by_complete_dates(X_arr, y_arr, ts_arr, sym_arr, max_samples):
    """Keep the most recent whole dates whose total rows fit under `max_samples`."""
    if len(X_arr) <= max_samples:
        return X_arr, y_arr, ts_arr, sym_arr
    unique_ts = np.sort(np.unique(ts_arr))[::-1]
    cumulative = 0
    keep_dates: list = []
    for ts in unique_ts:
        n = int((ts_arr == ts).sum())
        if cumulative + n > max_samples and keep_dates:
            break
        cumulative += n
        keep_dates.append(ts)
    keep_mask = np.isin(ts_arr, np.array(keep_dates))
    return X_arr[keep_mask], y_arr[keep_mask], ts_arr[keep_mask], sym_arr[keep_mask]


X_train, y_train, _train_ts, _train_sym = _trim_by_complete_dates(
    X_train, y_train, timestamps[train_mask], symbols[train_mask], MAX_TRAIN_SAMPLES
)
X_val, y_val, _val_ts, _val_sym = _trim_by_complete_dates(
    X_val, y_val, timestamps[val_mask], symbols[val_mask], MAX_VAL_SAMPLES
)
X_test, y_test, test_dates, test_symbols = _trim_by_complete_dates(
    X_test, y_test, test_dates, test_symbols, MAX_TEST_SAMPLES
)

print(f"Capped split: Train={len(X_train):,}, Val={len(X_val):,}, Test={len(X_test):,}")


# %% [markdown]
# ### Cross-sectional IC helper
#
# Mean cross-sectional Spearman IC by date - the same metric used across this section,
# so the image CNN is scored the way the sequence models were.
#
# A date's IC is undefined when a model predicts the same value for every fund on it:
# the predicted ranks are all tied and there is nothing to correlate. The library
# returns `NaN` for such a date, and polars treats `NaN` and null as different values,
# so `drop_nulls` alone leaves it in place and one of them makes the whole mean `NaN`.
# Both are filtered here, and the count of dates the mean was taken over is printed
# beside it - which matters here, because the subsampling above leaves far fewer dates
# than the notebooks that score on the full panel.


# %%
def cross_sectional_ic_mean(y_true, y_pred, dates, syms):
    """Mean cross-sectional Spearman IC over the dates where it is defined.

    Returns the mean and the defined/total date counts. Filters both null and NaN,
    since polars `drop_nulls` leaves NaN in place.
    """
    pred_df = pl.DataFrame({"timestamp": dates, "symbol": syms, "prediction": y_pred})
    ret_df = pl.DataFrame({"timestamp": dates, "symbol": syms, "forward_return": y_true})
    ic_per_date = cross_sectional_ic_series(
        pred_df,
        ret_df,
        pred_col="prediction",
        ret_col="forward_return",
        date_col="timestamp",
        entity_col="symbol",
    )
    defined = ic_per_date.filter(pl.col("ic").is_not_null() & pl.col("ic").is_not_nan())
    mean_ic = float(defined["ic"].mean()) if defined.height else float("nan")
    return {"ic": mean_ic, "n_defined": defined.height, "n_total": ic_per_date.height}


# %% [markdown]
# ## Gramian Angular Summation Field (GASF)
#
# The GASF encodes a time series as a matrix of trigonometric sums. Given a
# normalized series $\tilde{x}_i \in [-1, 1]$, we compute angles
# $\phi_i = \arccos(\tilde{x}_i)$ and form:
#
# $$\text{GASF}_{i,j} = \cos(\phi_i + \phi_j)$$
#
# This preserves temporal ordering. The diagonal evaluates to
# $\cos(2\phi_i) = 2\tilde{x}_i^2 - 1$ - a deterministic function of the
# normalized value, not the value itself - while off-diagonal entries capture
# pairwise angular relationships between time steps $i$ and $j$.


# %%
def gramian_angular_field(series: np.ndarray, image_size: int) -> np.ndarray:
    """Compute the Gramian Angular Summation Field (GASF).

    Args:
        series: 1D array of shape (T,) -- raw feature values for one window
        image_size: Output image dimension (image_size x image_size)

    Returns:
        GASF matrix of shape (image_size, image_size) with values in [-1, 1]
    """
    # Resample to image_size via linear interpolation
    target_positions = np.linspace(0, len(series) - 1, image_size)
    source_positions = np.arange(len(series))
    resampled = np.interp(target_positions, source_positions, series)

    # Min-max scale to [-1, 1]
    s_min, s_max = resampled.min(), resampled.max()
    if s_max - s_min < 1e-8:
        return np.zeros((image_size, image_size), dtype=np.float32)
    scaled = 2.0 * (resampled - s_min) / (s_max - s_min) - 1.0
    scaled = np.clip(scaled, -1.0, 1.0)

    # Compute angular representation
    phi = np.arccos(scaled)

    # GASF: cos(phi_i + phi_j)
    gasf = np.cos(phi[:, None] + phi[None, :])
    return gasf.astype(np.float32)


# %% [markdown]
# ## Markov Transition Field (MTF)
#
# The MTF discretizes a time series into $Q$ quantile bins and builds a
# transition matrix $W$ where $W_{q_i, q_j}$ is the probability of
# transitioning from bin $q_i$ to bin $q_j$. The full MTF matrix is:
#
# $$\text{MTF}_{i,j} = W_{q_i, q_j}$$
#
# where $q_i$ is the quantile bin of the $i$-th timestep. This captures the
# dynamic transition structure of the series.


# %%
def markov_transition_field(series: np.ndarray, image_size: int, n_bins: int = 8) -> np.ndarray:
    """Compute the Markov Transition Field (MTF).

    Args:
        series: 1D array of shape (T,) -- raw feature values for one window
        image_size: Output image dimension (image_size x image_size)
        n_bins: Number of quantile bins for discretization

    Returns:
        MTF matrix of shape (image_size, image_size) with values in [0, 1]
    """
    # Resample to image_size via linear interpolation
    target_positions = np.linspace(0, len(series) - 1, image_size)
    source_positions = np.arange(len(series))
    resampled = np.interp(target_positions, source_positions, series)

    # Discretize into quantile bins
    bin_edges = np.percentile(resampled, np.linspace(0, 100, n_bins + 1))
    bin_edges[-1] += 1e-8  # ensure max value is included
    bin_ids = np.digitize(resampled, bin_edges[1:-1])

    # Build transition matrix (n_bins x n_bins)
    transition = np.zeros((n_bins, n_bins), dtype=np.float32)
    for t in range(len(bin_ids) - 1):
        transition[bin_ids[t], bin_ids[t + 1]] += 1

    # Normalize rows to get probabilities
    row_sums = transition.sum(axis=1, keepdims=True)
    row_sums[row_sums == 0] = 1.0
    transition /= row_sums

    # Build MTF: entry (i, j) = transition probability from bin at time i to bin at time j
    # Vectorized via advanced indexing (equivalent to the nested loop but ~100x faster)
    mtf = transition[np.ix_(bin_ids, bin_ids)]

    return mtf.astype(np.float32)


# %% [markdown]
# ## Create Multi-Channel Image Dataset
#
# For each one-feature sequence, we encode the return history as a GASF and an MTF,
# then stack them as a 2-channel image. This gives the CNN both angular
# structure (GASF) and transition dynamics (MTF) as complementary views.
#
# > **Simplification**: This demo encodes only the five-day return history. A production
# > system would encode all features, stacking GASF+MTF per feature to produce a
# > $(2 \times F)$-channel image. We use one
# > feature here to keep encoding time manageable and focus on the method itself.


# %%
def create_image_dataset(X_sequences: np.ndarray, image_size: int) -> np.ndarray:
    """Convert feature sequences into stacked GAF + MTF image tensors.

    For each sample, takes the return feature and computes both GASF
    and MTF encodings, returning a (N, 2, H, W) tensor.

    Args:
        X_sequences: Array of shape (N, lookback, n_features)
        image_size: Target image dimension (H = W = image_size)

    Returns:
        Image tensor of shape (N, 2, image_size, image_size)
    """
    n_samples = X_sequences.shape[0]
    images = np.zeros((n_samples, 2, image_size, image_size), dtype=np.float32)

    for i in range(n_samples):
        series = X_sequences[i, :, 0]
        images[i, 0] = gramian_angular_field(series, image_size)
        images[i, 1] = markov_transition_field(series, image_size)

        if (i + 1) % 10000 == 0 or i == n_samples - 1:
            print(f"  Encoded {i + 1:,}/{n_samples:,} samples")

    return images


# %% [markdown]
# ## Encode Training, Validation, and Test Sets

# %%
print("Encoding training images...")
X_train_img = create_image_dataset(X_train, IMAGE_SIZE)
print(f"Train images: {X_train_img.shape}")

print("Encoding validation images...")
X_val_img = create_image_dataset(X_val, IMAGE_SIZE)
print(f"Val images: {X_val_img.shape}")

print("Encoding test images...")
X_test_img = create_image_dataset(X_test, IMAGE_SIZE)
print(f"Test images: {X_test_img.shape}")

# %% [markdown]
# ## Visualize Sample Encodings
#
# Inspecting the GASF and MTF channels for a few training samples to verify
# the encoding produces visually distinct patterns.

# %%
fig, axes = plt.subplots(3, 3, figsize=(10, 9), constrained_layout=True)

for row in range(3):
    idx = row * 1000  # spread samples across the dataset
    if idx >= len(X_train_img):
        idx = row

    # Raw feature series
    axes[row, 0].plot(X_train[idx, :, 0], linewidth=0.8)
    axes[row, 0].set_title(f"Sample {idx}: Raw Series" if row == 0 else f"Sample {idx}")
    axes[row, 0].set_xlabel("Timestep")

    # GASF channel
    im1 = axes[row, 1].imshow(X_train_img[idx, 0], cmap="RdBu_r", aspect="auto")
    axes[row, 1].set_title("GASF" if row == 0 else "")
    plt.colorbar(im1, ax=axes[row, 1], fraction=0.046)

    # MTF channel
    im2 = axes[row, 2].imshow(X_train_img[idx, 1], cmap="YlOrRd", aspect="auto")
    axes[row, 2].set_title("MTF" if row == 0 else "")
    plt.colorbar(im2, ax=axes[row, 2], fraction=0.046)

fig.suptitle("Three training windows, each as a series, a GASF and an MTF")
show_with_alt(
    fig,
    "A three-by-three grid, one row per training window. The left panel of each row "
    "plots the window as a line against timestep; the middle shows its GASF matrix on "
    "a blue-to-red diverging scale from minus one to one; the right shows its MTF "
    "matrix on a yellow-to-red scale from zero. Both matrices are square with one row "
    "and column per resampled position, and each carries its own colour bar.",
)

# %% [markdown]
# ## CNN Architecture
#
# Three convolutional blocks (Conv2d $\to$ BatchNorm $\to$ ReLU $\to$ MaxPool)
# followed by adaptive average pooling and a linear regression head.
# The architecture is intentionally simple to isolate the contribution of
# the image encoding from the model complexity.


# %%
class CNNBlock(nn.Module):
    """Single CNN building block: Conv2d -> BatchNorm -> ReLU -> MaxPool."""

    def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3):
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.pool(x)
        return x


# %% [markdown]
# ### Full Image CNN
#
# Three CNN blocks downsample the spatial dimensions by $2\times$ each,
# followed by adaptive average pooling to a fixed-size vector regardless
# of the input image size. Dropout before the final linear layer provides
# regularization.


# %%
class ImageCNN(nn.Module):
    """CNN for regression on GAF+MTF encoded time series images.

    Architecture:
        Input (N, 2, H, W)
        -> CNNBlock(2, 32)   -> (N, 32, H/2, W/2)
        -> CNNBlock(32, 64)  -> (N, 64, H/4, W/4)
        -> CNNBlock(64, 128) -> (N, 128, H/8, W/8)
        -> AdaptiveAvgPool2d(1, 1) -> (N, 128)
        -> Dropout -> Linear -> (N, 1)
    """

    def __init__(self, n_channels: int = 2, dropout: float = 0.5):
        super().__init__()
        self.block1 = CNNBlock(n_channels, 32)
        self.block2 = CNNBlock(32, 64)
        self.block3 = CNNBlock(64, 128)
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(dropout)
        self.fc = nn.Linear(128, 1)

    def forward(self, x):
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.pool(x)
        x = x.view(x.size(0), -1)  # flatten to (N, 128)
        x = self.dropout(x)
        return self.fc(x).squeeze(-1)  # (N,)


# %% [markdown]
# ## Train the Image CNN

# %%
set_global_seeds(SEED)
model = ImageCNN(n_channels=2, dropout=DROPOUT).to(DEVICE)

n_params = sum(p.numel() for p in model.parameters())
print(f"ImageCNN parameters: {n_params:,}")
print(
    f"Image size: {IMAGE_SIZE}x{IMAGE_SIZE}, channels: 2 (GASF + MTF), "
    f"from a {LOOKBACK}-day window: {'interpolated up' if IMAGE_SIZE > LOOKBACK else 'summarised down'}"
)

history = train_model(model, X_train_img, y_train, X_val_img, y_val, EPOCHS, LR, BATCH_SIZE, DEVICE)

# %% [markdown]
# ### Training convergence
#
# The two curves are what separates learning from memorising: a training curve alone
# cannot tell them apart, because both look like progress. A validation curve that
# stays flat or turns up while the training curve keeps falling says the CNN is fitting
# something in these images that does not carry to the next stretch of dates.

# %%
fig, ax = plt.subplots(figsize=(7, 4), constrained_layout=True)
epochs_axis = range(1, len(history["train_loss"]) + 1)
ax.plot(epochs_axis, history["train_loss"], marker="o", color=COLORS["blue"], label="Train")
ax.plot(epochs_axis, history["val_loss"], marker="o", color=COLORS["amber"], label="Validation")
ax.set_xlabel("Epoch")
ax.set_ylabel("MSE loss")
ax.legend()
add_message_title(
    ax,
    "Training and validation error per epoch",
    subtitle="GASF and MTF channels, forward 21-day return target",
)
show_with_alt(
    fig,
    "A line chart of mean squared error against epoch, with one line for the training "
    "set and one for the validation set, marked at each epoch.",
)

# %% [markdown]
# ## Evaluate on Test Set

# %%
model.eval()
with torch.no_grad():
    preds = []
    for i in range(0, len(X_test_img), INFER_BATCH_SIZE):
        X_test_t = torch.FloatTensor(X_test_img[i : i + INFER_BATCH_SIZE]).to(DEVICE)
        preds.append(model(X_test_t).cpu().numpy())
    y_pred = np.concatenate(preds, axis=0)

test_mse = np.mean((y_pred - y_test) ** 2)
cnn_ic = cross_sectional_ic_mean(y_test, y_pred, test_dates, test_symbols)
test_ic = cnn_ic["ic"]

print("\nImage CNN Test Results:")
print(f"  MSE: {test_mse:.6f}")
print(f"  Spearman IC: {test_ic:.4f}", end="")
print(f"  (defined on {cnn_ic['n_defined']} of {cnn_ic['n_total']} test dates)")

# %% [markdown]
# ## Ridge + PCA Baseline
#
# Flatten the 2-channel images into vectors, reduce dimensionality with PCA
# (100 components), then fit a Ridge regression. This tests whether the CNN
# learns spatial structure beyond what a linear model can extract from the
# same pixel representation.

# %%
X_train_flat = X_train_img.reshape(len(X_train_img), -1)
X_test_flat = X_test_img.reshape(len(X_test_img), -1)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train_flat)
X_test_scaled = scaler.transform(X_test_flat)

n_components = min(100, X_train_scaled.shape[1], X_train_scaled.shape[0])
pca = PCA(n_components=n_components, random_state=SEED)
X_train_pca = pca.fit_transform(X_train_scaled)
X_test_pca = pca.transform(X_test_scaled)

ridge = Ridge(alpha=1.0)
ridge.fit(X_train_pca, y_train)
y_ridge_pred = ridge.predict(X_test_pca)

ridge_mse = np.mean((y_ridge_pred - y_test) ** 2)
ridge_ic_result = cross_sectional_ic_mean(y_test, y_ridge_pred, test_dates, test_symbols)
ridge_ic = ridge_ic_result["ic"]
zero_mse = float(np.mean(y_test**2))

print(f"\nRidge + PCA ({n_components} components) Baseline:")
print(f"  MSE: {ridge_mse:.6f}")
print(f"  Spearman IC: {ridge_ic:.4f}", end="")
print(f"  (defined on {ridge_ic_result['n_defined']} of {ridge_ic_result['n_total']} test dates)")

# %% [markdown]
# ## The image CNN against the linear baseline
#
# Two questions, two panels. The left asks whether the model ordered the funds usefully
# on each date; the right asks whether its predicted return levels were closer than
# predicting zero. A model can do better on one and worse on the other, and both are
# reported because acting on a forecast uses the ordering while fitting one minimises
# the squared error.
#
# The baseline starts from the *same pixels*, reduces them to `n_components` principal
# components and maps those linearly to the label. Holding the encoding fixed on both
# sides is what makes the two comparable at all, but it does not isolate convolution:
# the two pipelines also differ in how many inputs they see, in whether the map is
# linear, and in how each is fitted and penalised. Read the bars as two pipelines
# built on one encoding, not as a measurement of what convolution contributed.
#
# Neither bar says anything about whether the encoding itself was worth doing. That
# would need a model fitted on the raw window, which the earlier notebooks in this
# section supply.

# %%
model_names = ["Image CNN", "Ridge + PCA"]
ic_values = [test_ic, ridge_ic]
mse_ratios = [test_mse / zero_mse, ridge_mse / zero_mse]
bar_palette = {"Image CNN": COLORS["blue"], "Ridge + PCA": COLORS["slate"]}

fig = make_subplots(
    rows=1,
    cols=2,
    subplot_titles=("Mean cross-sectional Spearman IC", "MSE relative to zero-return forecast"),
)
for model_name, ic_value, mse_ratio in zip(model_names, ic_values, mse_ratios, strict=True):
    fig.add_trace(
        go.Bar(
            x=[model_name],
            y=[ic_value],
            name=model_name,
            marker_color=bar_palette[model_name],
            text=[f"{ic_value:.3f}"],
            textposition="outside",
            showlegend=False,
        ),
        row=1,
        col=1,
    )
    fig.add_trace(
        go.Bar(
            x=[model_name],
            y=[mse_ratio],
            name=model_name,
            marker_color=bar_palette[model_name],
            text=[f"{mse_ratio:.2f}x"],
            textposition="outside",
            showlegend=False,
        ),
        row=1,
        col=2,
    )

fig.add_hline(y=0, line_color=COLORS["neutral"], row=1, col=1)
fig.add_hline(y=1, line_dash="dot", line_color=COLORS["neutral"], row=1, col=2)
fig.update_layout(
    title="The image CNN and ridge on the same pixels, ranked and levelled",
    height=480,
)
fig.update_yaxes(title_text="Spearman IC", row=1, col=1)
fig.update_yaxes(title_text="MSE / zero-return MSE", row=1, col=2)
show_plotly_with_alt(
    fig,
    "Two bar panels, one bar per model. The left panel gives each model's mean "
    "cross-sectional Spearman IC against a line at zero; the right gives its test MSE "
    "as a multiple of the zero forecast's, against a dotted line at one.",
)

# %% [markdown]
# ## What this setup can and cannot be asked
#
# The input here is one feature - `FEATURE_COLS` holds a single column - where every
# other architecture in this section reads eight. That is not an oversight to correct
# later; it is what keeps the encoding legible, since a GASF of one series is a
# picture you can look at and a GASF of eight is a stack you cannot. It also means the
# bars above compare two models on a deliberately thin representation.
#
# The sample is capped the same way `07_mamba_ssm` caps it, so both scores are averaged
# over the printed number of dates rather than the whole test stretch. Between the
# single feature and the capped dates, this notebook is a demonstration of the
# encoding, and the published comparisons that encode every feature and score against
# sequence-native models are the tests that would say something about GAF and MTF as a
# representation.

# %% [markdown]
# ## Key takeaways
#
# 1. **Both encodings normalise inside the window, so scale is gone.** The GASF
#    min-max scales to $[-1, 1]$ before taking angles, and the MTF assigns quantile
#    bins computed from that window alone. Two windows with the same shape and
#    different volatility produce the same picture, and the label is a return, whose
#    scale is exactly what was removed. Any cross-sectional ranking has to come from
#    shape.
# 2. **Every cell is a pair of positions, not a position.** A GASF cell holds a
#    function of the angles at $i$ and $j$; an MTF cell holds how often the series
#    moved between the bins those two positions occupy. A convolution over the image
#    therefore reads neighbourhoods of *pairs*, which is a different object from the
#    neighbourhoods of timesteps a 1D convolution reads.
# 3. **The resampling can add pixels that are not data.** The window is interpolated
#    onto an `IMAGE_SIZE` grid before encoding; when `IMAGE_SIZE` exceeds `LOOKBACK`,
#    the extra rows and columns are interpolation, and the image is larger than the
#    information in it. Check the two constants against each other before treating
#    image size as a capacity knob.
# 4. **Encoding is per-sample work done before training.** The images are built in a
#    loop over windows and materialised in full, which is a preprocessing cost and a
#    memory cost that feeding the raw sequence to a recurrent or attention model does
#    not incur.
# 5. **One encoding, two pipelines - and that is all the comparison is.** Both models
#    start from the same images, which is what makes them comparable, but they differ
#    in more than convolution: the ridge sees `n_components` principal components
#    rather than every pixel, fits a linear map, and is penalised and solved
#    differently. Attributing the gap to any single one of those differences would be
#    reading past the experiment. Whether the encoding itself was worth doing is a
#    further question again, and needs a model fitted on the raw window.
#
# **Known limitations.** One feature, one chronological split, one label horizon, one
# seed, and a sample capped by the encoding cost. Deterministic PyTorch algorithms, a
# fixed cuBLAS workspace and an explicit PCA random state make repeated execution
# reproduce on the same software and GPU; another environment will differ in the final
# decimals.
#
# **Next**: `09_foundation_models` stops training on this panel altogether and asks
# what a model pretrained on other series brings to it.

```