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PCA now fit and transform over all trajectories. Networks now have

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start-time properties for that to control where plotting starts. Added distance from
parent table/matrix.
This commit is contained in:
Maximilian Zorn
2021-05-21 15:28:09 +02:00
parent f5ca3d1115
commit 1e8ccd2b8b
3 changed files with 110 additions and 35 deletions
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68
visualization.py
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@ -73,7 +73,7 @@ def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory:
def plot_3d(matrices_weights_history, directory: Union[str, Path], population_size, z_axis_legend,
exp_name="experiment", is_trained="", batch_size=1):
exp_name="experiment", is_trained="", batch_size=1, plot_pca_together=True):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@ -83,26 +83,58 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
pca = PCA(n_components=2, whiten=True)
ax = plt.axes(projection='3d')
loop_matrices_weights_history = tqdm(range(len(matrices_weights_history)))
for i in loop_matrices_weights_history:
loop_matrices_weights_history.set_description("Plotting weights 3D PCA %s" % i)
if plot_pca_together:
weight_histories = []
start_times = []
weight_matrix, start_time = matrices_weights_history[i]
weight_matrix = np.array(weight_matrix)
n, x, y = weight_matrix.shape
weight_matrix = weight_matrix.reshape(n, x * y)
for wh, st in matrices_weights_history:
start_times.append(st)
wm = np.array(wh)
n, x, y = wm.shape
wm = wm.reshape(n, x * y)
#print(wm.shape, wm)
weight_histories.append(wm)
pca.fit(weight_matrix)
weight_matrix_pca = pca.transform(weight_matrix)
weight_data = np.array(weight_histories)
n, x, y = weight_data.shape
weight_data = weight_data.reshape(n*x, y)
pca.fit(weight_data)
weight_data_pca = pca.transform(weight_data)
xdata, ydata = [], []
for j in range(len(weight_matrix_pca)):
xdata.append(weight_matrix_pca[j][0])
ydata.append(weight_matrix_pca[j][1])
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
for transformed_trajectory, start_time in zip(np.split(weight_data_pca, n), start_times):
start_log_time = int(start_time / batch_size)
#print(start_time, start_log_time)
xdata = transformed_trajectory[start_log_time:, 0]
ydata = transformed_trajectory[start_log_time:, 1]
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
ax.plot3D(xdata, ydata, zdata, label=f"net")
ax.scatter(xdata, ydata, zdata, s=7)
else:
loop_matrices_weights_history = tqdm(range(len(matrices_weights_history)))
for i in loop_matrices_weights_history:
loop_matrices_weights_history.set_description("Plotting weights 3D PCA %s" % i)
ax.plot3D(xdata, ydata, zdata)
ax.scatter(np.array(xdata), np.array(ydata), np.array(zdata), s=7)
weight_matrix, start_time = matrices_weights_history[i]
weight_matrix = np.array(weight_matrix)
n, x, y = weight_matrix.shape
weight_matrix = weight_matrix.reshape(n, x * y)
pca.fit(weight_matrix)
weight_matrix_pca = pca.transform(weight_matrix)
xdata, ydata = [], []
start_log_time = int(start_time / 10)
for j in range(start_log_time, len(weight_matrix_pca)):
xdata.append(weight_matrix_pca[j][0])
ydata.append(weight_matrix_pca[j][1])
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
ax.plot3D(xdata, ydata, zdata, label=f"net {i}")
ax.scatter(np.array(xdata), np.array(ydata), np.array(zdata), s=7)
steps = mpatches.Patch(color="white", label=f"{z_axis_legend}: {len(matrices_weights_history)} steps")
population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks")
@ -146,7 +178,7 @@ def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Pa
loop_nets_array.set_description("Creating ST weights history %s" % i)
matrices_weights_history.append( (nets_array[i].s_train_weights_history, nets_array[i].start_time) )
z_axis_legend = "epochs"
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size)