Update pytorch plots (#177)

+ update afr plots for power example
simplymathematics · Mar 28, 2024 · 104f954 · 104f954
1 parent 9b1de2f
commit 104f954
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Showing 18 changed files with 3,080 additions and 2,837 deletions.
diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py
diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py
@@ -49,6 +49,8 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra
     path_gen.sort()
     path_gen = list(set(path_gen))
     path_gen.sort()
+    path_gen = list(set(path_gen))
+    path_gen.sort()
     folder_gen = map(lambda x: x.parent, path_gen)
     folder_gen = set(folder_gen)
     results = {}
@@ -59,6 +61,8 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra
     df = pd.DataFrame(results).T
     df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
     df.columns = df.columns.str.strip()
+    df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)
+    df.columns = df.columns.str.strip()
     return df
 
 

diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py
@@ -37,6 +37,8 @@ def cat_plot(
     file,
     folder,
     legend_title=None,
+    x_lim=None,
+    y_lim=None,
     hue_order=None,
     rotation=0,
     set={},
@@ -122,6 +124,10 @@ def cat_plot(
             pass
     graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation)
     graph.set(**set)
+    if x_lim is not None:
+        graph.set(xlim=x_lim)
+    if y_lim is not None:
+        graph.set(ylim=y_lim)
     graph.tight_layout()
     graph.savefig(folder / file)
     plt.gcf().clear()
@@ -229,6 +235,8 @@ def scatter_plot(
     folder,
     y_scale=None,
     x_scale=None,
+    x_lim=None,
+    y_lim=None,
     legend={},
     hue_order=None,
     filetype=".eps",
@@ -296,6 +304,10 @@ def scatter_plot(
         graph.set_yscale(y_scale)
     if x_scale is not None:
         graph.set_xscale(x_scale)
+    if x_lim is not None:
+        graph.set_xlim(x_lim)
+    if y_lim is not None:
+        graph.set_ylim(y_lim)
     graph.set_xlabel(xlabel)
     graph.set_ylabel(ylabel)
     graph.legend(**legend)
@@ -363,6 +375,7 @@ def main(args):
     logger.info(f"Creating folder {FOLDER}")
     FOLDER.mkdir(parents=True, exist_ok=True)
     logger.info(f"Saving data to {FOLDER }")
+    logger.info(f"Saving data to {FOLDER }")
     IMAGE_FILETYPE = (
         args.plotfiletype
         if args.plotfiletype.startswith(".")
@@ -382,6 +395,7 @@ def main(args):
     for dict_ in line_plot_list:
         line_plot(data, **dict_, folder=FOLDER, filetype=IMAGE_FILETYPE)
 
+    scatter_plot_list = big_dict.get("scatter_plot", [])
     scatter_plot_list = big_dict.get("scatter_plot", [])
     for dict_ in scatter_plot_list:
         scatter_plot(data, **dict_, folder=FOLDER, filetype=IMAGE_FILETYPE)

diff --git a/examples/gzip/.gitignore b/examples/gzip/.gitignore
@@ -1,11 +1,15 @@
 output
 *.db
 kdd_nsl
-truthseeker
-ddos
-sms_spam
 /raw_data
 .dvc/config
-2-22/
-2-28/
-/params.yaml
+.dvc/*
+2-22/*
+2-28/*
+3-7/*
+gzip/*
+ddos/*
+kdd_nsl/*
+sms_spam/*
+truthseeker/*
+conf/*/best_*.yaml
diff --git a/examples/power/.dvc/config b/examples/power/.dvc/config
diff --git a/examples/power/conf/afr.yaml b/examples/power/conf/afr.yaml
@@ -16,21 +16,22 @@ weibull:
   plot:
     file : weibull_aft.pdf
     title : Weibull AFR Model
-  labels:
-    "Intercept: rho_": "$\\rho$"
-    "Intercept: lambda_": "$\\lambda$" 
-    "data.sample.random_state: lambda_": "Random State"
-    "atk_value: lambda_": "Attack Strength"
-    "train_time: lambda_": "$t_{train}$"
-    "predict_proba_time: lambda_": "$t_{predict}$"
-    "adv_accuracy: lambda_": "Adv. Accuracy"
-    "accuracy: lambda_": "Ben. Accuracy"
-    "adv_fit_time: lambda_": "$t_{attack}$"
-    "adv_failure_rate: lambda_": "$h_{adv.}(t;\\theta)$"
-    "failure_rate: lambda_": "$h_{ben.}(t;\\theta)$"
-    "model.trainer.nb_epoch: lambda_": "No. of Epochs"
-    "model.trainer.batch_size: lambda_": "Batch Size"
-    "def_gen": "Defence"
+    labels:
+      "Intercept: rho_": "$\\rho$"
+      "Intercept: lambda_": "$\\lambda$" 
+      "data.sample.random_state: lambda_": "Random State"
+      "atk_value: lambda_": "Attack Strength"
+      "train_time: lambda_": "$t_{train}$"
+      "predict_proba_time: lambda_": "$t_{predict}$"
+      "adv_accuracy: lambda_": "Adv. Accuracy"
+      "accuracy: lambda_": "Ben. Accuracy"
+      "adv_fit_time: lambda_": "$t_{attack}$"
+      "adv_failure_rate: lambda_": "$h_{adv.}(t;\\theta)$"
+      "failure_rate: lambda_": "$h_{ben.}(t;\\theta)$"
+      "model.trainer.nb_epoch: lambda_": "No. of Epochs"
+      "model.trainer.batch_size: lambda_": "Batch Size"
+      "def_gen": "Defence"
+      "attack.init.eps: lambda_": "$\\varepsilon$"
   partial_effect:
     - "file": "weibull_epochs_partial_effect.pdf"
       "covariate_array": "model.trainer.nb_epoch"
@@ -46,21 +47,20 @@ cox:
   plot:
     file : cox_aft.pdf
     title : Cox AFR Model
-  labels:
-    "Intercept: rho_": "$\\rho$"
-    "Intercept: lambda_": "$\\lambda$" 
-    "data.sample.random_state: lambda_": "Random State"
-    "atk_value: lambda_": "Attack Strength"
-    "train_time: lambda_": "$t_{train}$"
-    "predict_proba_time: lambda_": "$t_{predict}$"
-    "adv_accuracy: lambda_": "Adv. Accuracy"
-    "accuracy: lambda_": "Ben. Accuracy"
-    "adv_fit_time: lambda_": "$t_{attack}$"
-    "adv_failure_rate: lambda_": "$h_{adv.}(t;\\theta)$"
-    "failure_rate: lambda_": "$h_{ben.}(t;\\theta)$"
-    "model.trainer.nb_epoch: lambda_": "No. of Epochs"
-    "model.trainer.batch_size: lambda_": "Batch Size"
-    "def_gen": "Defence"
+    labels:
+      "data.sample.random_state": "Random State"
+      "atk_value": "Attack Strength"
+      "train_time": "$t_{train}$"
+      "predict_proba_time": "$t_{predict}$"
+      "adv_accuracy": "Adv. Accuracy"
+      "accuracy": "Ben. Accuracy"
+      "adv_fit_time": "$t_{attack}$"
+      "adv_failure_rate": "$h_{adv.}(t;\\theta)$"
+      "failure_rate": "$h_{ben.}(t;\\theta)$"
+      "model.trainer.nb_epoch": "No. of Epochs"
+      "model.trainer.batch_size": "Batch Size"
+      "def_gen": "Defence"
+      "attack.init.eps": "$\\varepsilon$"
   partial_effect:
     - "file": "cox_epochs_partial_effect.pdf"
       "covariate_array": "model.trainer.nb_epoch"
@@ -76,21 +76,22 @@ log_logistic:
   plot:
     file : log_logistic_aft.pdf
     title : Log logistic AFR Model
-  labels:
-    "Intercept: rho_": "$\\rho$"
-    "Intercept: lambda_": "$\\lambda$" 
-    "data.sample.random_state: lambda_": "Random State"
-    "atk_value: lambda_": "Attack Strength"
-    "train_time: lambda_": "$t_{train}$"
-    "predict_proba_time: lambda_": "$t_{predict}$"
-    "adv_accuracy: lambda_": "Adv. Accuracy"
-    "accuracy: lambda_": "Ben. Accuracy"
-    "adv_fit_time: lambda_": "$t_{attack}$"
-    "adv_failure_rate: lambda_": "$h_{adv.}(t;\\theta)$"
-    "failure_rate: lambda_": "$h_{ben.}(t;\\theta)$"
-    "model.trainer.nb_epoch: lambda_": "No. of Epochs"
-    "model.trainer.batch_size: lambda_": "Batch Size"
-    "def_gen": "Defence"
+    labels:
+      "Intercept: beta_": "$\\beta$"
+      "Intercept: alpha_": "$\\alpha$"
+      "data.sample.random_state: alpha_": "Random State"
+      "atk_value: alpha_": "Attack Strength"
+      "train_time: alpha_": "$t_{train}$"
+      "predict_proba_time: alpha_": "$t_{predict}$"
+      "adv_accuracy: alpha_": "Adv. Accuracy"
+      "accuracy: alpha_": "Ben. Accuracy"
+      "adv_fit_time: alpha_": "$t_{attack}$"
+      "adv_failure_rate: alpha_": "$h_{adv.}(t;\\theta)$"
+      "failure_rate: alpha_": "$h_{ben.}(t;\\theta)$"
+      "model.trainer.nb_epoch: alpha_": "No. of Epochs"
+      "model.trainer.batch_size: alpha_": "Batch Size"
+      "def_gen": "Defence"
+      "attack.init.eps: alpha_": "$\\varepsilon$"
   partial_effect:
     - "file": "log_logistic_epochs_partial_effect.pdf"
       "covariate_array": "model.trainer.nb_epoch"
@@ -106,21 +107,22 @@ log_normal:
   plot:
     file : log_normal_aft.pdf
     title : Log Normal AFR Model
-  labels:
-    "Intercept: rho_": "$\\rho$"
-    "Intercept: lambda_": "$\\lambda$" 
-    "data.sample.random_state: lambda_": "Random State"
-    "atk_value: lambda_": "Attack Strength"
-    "train_time: lambda_": "$t_{train}$"
-    "predict_proba_time: lambda_": "$t_{predict}$"
-    "adv_accuracy: lambda_": "Adv. Accuracy"
-    "accuracy: lambda_": "Ben. Accuracy"
-    "adv_fit_time: lambda_": "$t_{attack}$"
-    "adv_failure_rate: lambda_": "$h_{adv.}(t;\\theta)$"
-    "failure_rate: lambda_": "$h_{ben.}(t;\\theta)$"
-    "model.trainer.nb_epoch: lambda_": "No. of Epochs"
-    "model.trainer.batch_size: lambda_": "Batch Size"
-    "def_gen": "Defence"
+    labels:
+      "Intercept: sigma_": "$\\sigma$" 
+      "Intercept: mu_": "$\\mu$"
+      "atk_value: mu_": "Attack Strength"
+      "train_time: mu_": "$t_{train}$"
+      "predict_proba_time: mu_": "$t_{predict}$"
+      "adv_accuracy: mu_": "Adv. Accuracy"
+      "accuracy: mu_": "Ben. Accuracy"
+      "adv_fit_time: mu_": "$t_{attack}$"
+      "adv_failure_rate: mu_": "$h_{adv.}(t;\\theta)$"
+      "failure_rate: mu_": "$h_{ben.}(t;\\theta)$"
+      "model.trainer.nb_epoch: mu_": "No. of Epochs"
+      "model.trainer.batch_size: mu_": "Batch Size"
+      "def_gen": "Defence"
+      "attack.init.eps: mu_": "$\\varepsilon$"
+      "data.sample.random_state: mu_": "Random State"
   partial_effect:
     - "file": "log_normal_epochs_partial_effect.pdf"
       "covariate_array": "model.trainer.nb_epoch"