99 lines
4.7 KiB
Python
99 lines
4.7 KiB
Python
import pandas as pd
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from pathlib import Path
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import torch
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import numpy as np
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from network import MetaNet, FixTypes
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import matplotlib.pyplot as plt
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from sklearn.decomposition import PCA
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def plot_single_3d_trajectories_by_layer(model:MetaNet, all_weights:pd.DataFrame, save_path:Path, status_type:FixTypes):
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''' This plots one PCA for every net (over its n epochs) as one trajectory and then combines all of them in one plot '''
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all_epochs = all_weights.Epoch.unique()
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pca = PCA(n_components=2, whiten=True)
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save_path.mkdir(exist_ok=True, parents=True)
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for layer_idx, model_layer in enumerate(model.all_layers):
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fixpoint_statuses = [net.is_fixpoint for net in model_layer.particles]
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num_status_of_layer = sum([net.is_fixpoint == status_type for net in model_layer.particles])
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layer = all_weights[all_weights.Weight.str.startswith(f"L{layer_idx}")]
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weight_batches = [np.array(layer[layer.Weight == name].values.tolist())[:,2:] for name in layer.Weight.unique()]
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fig = plt.figure()
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ax = plt.axes(projection='3d')
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fig.set_figheight(10)
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fig.set_figwidth(12)
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plt.tight_layout()
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for weights_of_net, status in zip(weight_batches, fixpoint_statuses):
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if status == status_type:
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pca.fit(weights_of_net)
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transformed_trajectory = pca.transform(weights_of_net)
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xdata = transformed_trajectory[:, 0]
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ydata = transformed_trajectory[:, 1]
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zdata = all_epochs
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ax.plot3D(xdata, ydata, zdata)
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ax.scatter(xdata, ydata, zdata, s=7)
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ax.set_title(f"Layer {layer_idx}: {num_status_of_layer}-{status_type}", fontsize=20)
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ax.set_xlabel('PCA Transformed x-axis', fontsize=20)
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ax.set_ylabel('PCA Transformed y-axis', fontsize=20)
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ax.set_zlabel('Epochs', fontsize=30, rotation = 0)
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file_path = save_path / f"layer_{layer_idx}_{num_status_of_layer}_{status_type}.png"
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plt.savefig(file_path, bbox_inches="tight", dpi=300, format="png")
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plt.clf()
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def plot_grouped_3d_trajectories_by_layer(model:MetaNet, all_weights:pd.DataFrame, save_path:Path, status_type:FixTypes):
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''' This computes the PCA over all the net-weights at once and then plots that.'''
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all_epochs = all_weights.Epoch.unique()
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pca = PCA(n_components=2, whiten=True)
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save_path.mkdir(exist_ok=True, parents=True)
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for layer_idx, model_layer in enumerate(model.all_layers):
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fixpoint_statuses = [net.is_fixpoint for net in model_layer.particles]
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num_status_of_layer = sum([net.is_fixpoint == status_type for net in model_layer.particles])
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layer = all_weights[all_weights.Weight.str.startswith(f"L{layer_idx}")]
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weight_batches = np.vstack([np.array(layer[layer.Weight == name].values.tolist())[:,2:] for name in layer.Weight.unique()])
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fig = plt.figure()
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fig.set_figheight(10)
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fig.set_figwidth(12)
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ax = plt.axes(projection='3d')
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plt.tight_layout()
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pca.fit(weight_batches)
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w_transformed = pca.transform(weight_batches)
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for transformed_trajectory,status in zip(np.split(w_transformed, len(layer.Weight.unique())), fixpoint_statuses):
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if status == status_type:
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xdata = transformed_trajectory[:, 0]
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ydata = transformed_trajectory[:, 1]
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zdata = all_epochs
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ax.plot3D(xdata, ydata, zdata)
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ax.scatter(xdata, ydata, zdata, s=7)
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ax.set_title(f"Layer {layer_idx}: {num_status_of_layer}-{status_type}", fontsize=20)
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ax.set_xlabel('PCA Transformed x-axis', fontsize=20)
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ax.set_ylabel('PCA Transformed y-axis', fontsize=20)
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ax.set_zlabel('Epochs', fontsize=30, rotation = 0)
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file_path = save_path / f"layer_{layer_idx}_{num_status_of_layer}_{status_type}_grouped.png"
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plt.savefig(file_path, bbox_inches="tight", dpi=300, format="png")
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plt.clf()
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if __name__ == '__main__':
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weight_path = Path("weight_store.csv")
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model_path = Path("trained_model_ckpt_e100.tp")
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save_path = Path("figures/3d_trajectories/")
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weight_df = pd.read_csv(weight_path)
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weight_df = weight_df.drop_duplicates(subset=['Weight','Epoch'])
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model = torch.load(model_path, map_location=torch.device('cpu'))
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plot_single_3d_trajectories_by_layer(model, weight_df, save_path, status_type=FixTypes.identity_func)
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plot_single_3d_trajectories_by_layer(model, weight_df, save_path, status_type=FixTypes.other_func)
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#plot_grouped_3d_trajectories_by_layer(model, weight_df, save_path, FixTypes.identity_func)
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#plot_grouped_3d_trajectories_by_layer(model, weight_df, save_path, FixTypes.other_func) |