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a84194a d042c14 a84194a c717e13 a84194a d042c14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_theme(style="whitegrid", font_scale=1)
this_graph_palette = sns.color_palette("husl", 4)
sns.set_palette(this_graph_palette)
def plotting_learning_curve(epoch_error, element_learning_rate, model_learning_rate):
fig, ax = plt.subplots(1, 2, figsize=(8, 4))
ax[0].plot(
epoch_error["epoch"],
epoch_error["error"],
label="Training",
linewidth=2
)
ax[0].plot(
epoch_error["epoch"],
epoch_error["validate_error"],
label="Validation", linewidth=2
)
ax[0].set_title(
f"Learning Curve {model_learning_rate}/{element_learning_rate} in MSE",
fontdict={"fontsize": 12},
)
ax[1].plot(
epoch_error["epoch"],
epoch_error["error_percent"],
label="Training",
linewidth=2
)
ax[1].plot(
epoch_error["epoch"],
epoch_error["validate_error_percent"],
label="Validation", linewidth=2
)
ax[1].set_ylim(
0, 100)
ax[1].set_xlim(
0, 100)
ax[1].set_title(
f"Learning Curve {model_learning_rate}/{element_learning_rate} in RMSPE",
fontdict={"fontsize": 12},
)
ax[0].legend(loc="lower right")
ax[1].legend(loc="lower right")
fig.tight_layout(pad=3.0)
def plot_model_level_weight(adjustment_data, epoch_error):
fig, ax = plt.subplots(1, 3, figsize=(14, 3))
ax[0].plot(
adjustment_data["model_weight_1"],
label="Material Element Weight",
)
ax[0].plot(
adjustment_data["model_weight_2"],
label="Labor Element Weight",
)
ax[0].plot(
adjustment_data["model_weight_3"],
label="Utiltiy Cost Element Weight",
)
best_train_epoch = epoch_error[
epoch_error["error_percent"]
== epoch_error["error_percent"].min()
]
best_validate_epoch = epoch_error[
epoch_error["validate_error_percent"]
== epoch_error["validate_error_percent"].min()
]
try:
best_train_epoch = best_train_epoch.iloc[0]
best_train_epoch = best_train_epoch["epoch"]
best_validate_epoch = best_validate_epoch.iloc[0]
best_validate_epoch = best_validate_epoch["epoch"]
except Exception as e:
best_train_epoch = np.nan
best_validate_epoch = np.nan
ax[0].set_xlabel(
f'Train Err {epoch_error.iloc[-1]["error_percent"]:.2f} , Validate Err {epoch_error.iloc[-1]["validate_error_percent"]:.2f} \n Best Epoch Train {best_train_epoch:.0f} , Validate {best_validate_epoch:.0f}'
)
ax[0].set_xticks([])
ax[0].legend(loc="lower right")
ax[0].set_title(
"All Model Level Weight",
fontdict={"fontsize": 12},
)
ax[1].plot(
adjustment_data["model_weight_1"],
label="Material Element Weight",
)
ax[1].legend(loc="lower right")
ax[1].set_title(
"Material Element Weight",
fontdict={"fontsize": 12},
)
ax[2].plot(
adjustment_data["model_weight_2"],
label="Labor Element Weight",
)
ax[2].plot(
adjustment_data["model_weight_3"],
label="Utiltiy Cost Element Weight",
)
ax[2].legend(loc="lower right")
ax[2].set_title(
"Labor and Utility Cost Element Weight",
fontdict={"fontsize": 12},
)
def plot_element_level_weight(adjustment_data, material_columns, labor_columns, utility_columns):
fig, ax = plt.subplots(1, 3, figsize=(14, 3))
for column in material_columns:
ax[0].plot(
adjustment_data[column],
label=column,
)
for column in labor_columns:
ax[1].plot(
adjustment_data[column],
label=column,
)
for column in utility_columns:
ax[2].plot(
adjustment_data[column],
label=column,
)
ax[0].legend(loc="lower right")
ax[2].legend(loc="lower right")
ax[0].set_title(
"Material Element Weight",
fontdict={"fontsize": 12},
)
ax[1].set_title(
"Labor Element Weight",
fontdict={"fontsize": 12},
)
ax[2].set_title(
"Utility Cost Element Weight",
fontdict={"fontsize": 12},
)
fig.tight_layout(pad=2.0)
|