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functionalities_test.py updated

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steffen-illium
2021-05-16 14:51:21 +02:00
parent b1472479cb
commit 36377ee27d
7 changed files with 66 additions and 97 deletions
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visualization.py
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@ -1,6 +1,5 @@
from pathlib import Path
from tokenize import String
from typing import List, Dict
from typing import List, Dict, Union
from tqdm import tqdm
import matplotlib.pyplot as plt
@ -19,7 +18,7 @@ def plot_output(output):
plt.show()
def plot_loss(loss_array, directory, batch_size=1):
def plot_loss(loss_array, directory: Union[str, Path], batch_size=1):
""" Plotting the evolution of the loss function."""
fig = plt.figure()
@ -41,8 +40,8 @@ def plot_loss(loss_array, directory, batch_size=1):
plt.clf()
def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory: String, learning_rate: float,
exp_details: String, source_check=None):
def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory: Union[str, Path], learning_rate: float,
exp_details: str, source_check=None):
""" Plotting the number of fixpoints in a barchart. """
fig = plt.figure()
@ -73,8 +72,8 @@ def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory:
plt.clf()
def plot_3d(matrices_weights_history, directory, population_size, z_axis_legend, exp_name="experiment", is_trained="",
batch_size=1):
def plot_3d(matrices_weights_history, directory: Union[str, Path], population_size, z_axis_legend,
exp_name="experiment", is_trained="", batch_size=1):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@ -109,7 +108,10 @@ def plot_3d(matrices_weights_history, directory, population_size, z_axis_legend,
population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks")
if z_axis_legend == "Self-application":
trained = mpatches.Patch(color="white", label=f"Trained: true") if is_trained == "_trained" else mpatches.Patch(color="white", label=f"Trained: false")
if is_trained == '_trained':
trained = mpatches.Patch(color="white", label=f"Trained: true")
else:
trained = mpatches.Patch(color="white", label=f"Trained: false")
ax.legend(handles=[steps, population_size, trained])
else:
ax.legend(handles=[steps, population_size])
@ -134,7 +136,7 @@ def plot_3d(matrices_weights_history, directory, population_size, z_axis_legend,
plt.show()
def plot_3d_self_train(nets_array: List, exp_name: String, directory: String, batch_size: int):
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int):
""" Plotting the evolution of the weights in a 3D space when doing self training. """
matrices_weights_history = []
@ -150,7 +152,7 @@ def plot_3d_self_train(nets_array: List, exp_name: String, directory: String, ba
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size)
def plot_3d_self_application(nets_array: List, exp_name: String, directory_name: String, batch_size: int) -> None:
def plot_3d_self_application(nets_array: List, exp_name: str, directory_name: Union[str, Path], batch_size: int) -> None:
""" Plotting the evolution of the weights in a 3D space when doing self application. """
matrices_weights_history = []
@ -171,7 +173,7 @@ def plot_3d_self_application(nets_array: List, exp_name: String, directory_name:
plot_3d(matrices_weights_history, directory_name, len(nets_array), z_axis_legend, exp_name, is_trained, batch_size)
def plot_3d_soup(nets_list, exp_name, directory):
def plot_3d_soup(nets_list, exp_name, directory: Union[str, Path]):
""" Plotting the evolution of the weights in a 3D space for the soup environment. """
# This batch size is not relevant for soups. To not affect the number of epochs shown in the 3D plot,
@ -182,7 +184,7 @@ def plot_3d_soup(nets_list, exp_name, directory):
def line_chart_fixpoints(fixpoint_counters_history: list, epochs: int, ST_steps_between_SA: int,
SA_steps, directory: String, population_size: int):
SA_steps, directory: Union[str, Path], population_size: int):
""" Plotting the percentage of fixpoints after each iteration of SA & ST steps. """
fig = plt.figure()
@ -211,7 +213,7 @@ def line_chart_fixpoints(fixpoint_counters_history: list, epochs: int, ST_steps_
plt.clf()
def box_plot(data, directory, population_size):
def box_plot(data, directory: Union[str, Path], population_size):
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 7))
# ax = fig.add_axes([0, 0, 1, 1])
@ -232,7 +234,7 @@ def box_plot(data, directory, population_size):
plt.clf()
def write_file(text, directory):
def write_file(text, directory: Union[str, Path]):
directory = Path(directory)
filepath = directory / 'experiment.txt'
with filepath.open('w+') as f: