From 5a15139dadde8d923703ece93745fa250b1a0c53 Mon Sep 17 00:00:00 2001
From: Millian Poquet <millian.poquet@irit.fr>
Date: Fri, 17 May 2024 11:50:18 +0200
Subject: [PATCH] prediction notebook: shrink hists (py->R)
---
notebooks/prediction-results-analysis.Rmd | 101 ++++++++--------------
1 file changed, 37 insertions(+), 64 deletions(-)
diff --git a/notebooks/prediction-results-analysis.Rmd b/notebooks/prediction-results-analysis.Rmd
index e20a77b..6398e33 100644
--- a/notebooks/prediction-results-analysis.Rmd
+++ b/notebooks/prediction-results-analysis.Rmd
@@ -28,7 +28,7 @@ result_filenames = os.listdir(RESULTS_PATH)
df_all_results = pd.concat([pd.read_csv(RESULTS_PATH+filename, low_memory=False) for filename in result_filenames])
df_all_results = df_all_results.dropna(subset=PRED_COLS)
-df_all_results
+df_all_results.to_csv('/tmp/allresults-mean.csv', index=False)
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
@@ -97,6 +97,7 @@ g.set_ylabel("Prediction Method")
g.set_xlabel("Mean Absolute Percentage Error (MAPE) ")
plt.tight_layout(pad=0)
plt.savefig("./fig3a-pred-mape-mean-power.svg")
+plt.savefig("./fig3a-pred-mape-mean-power.pdf")
```
## Processing the max power prediction results
@@ -122,6 +123,7 @@ result_filenames = os.listdir(RESULTS_PATH)
df_all_results = pd.concat([pd.read_csv(RESULTS_PATH+filename, low_memory=False) for filename in result_filenames])
df_all_results = df_all_results.dropna(subset=PRED_COLS)
+df_all_results.to_csv('/tmp/allresults-max.csv', index=False)
#df_all_results
@@ -193,73 +195,44 @@ g.set_ylabel("Prediction Method")
g.set_xlabel("Mean Absolute Percentage Error (MAPE)")
plt.tight_layout(pad=0)
plt.savefig("./fig3b-pred-mape-max-power.svg")
+plt.savefig("./fig3b-pred-mape-max-power.pdf")
```
## Getting the actual mean and max power distributions
-### Mean: Figure 2 (a)
```{python}
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-TINY_SIZE = 2
-SMALL_SIZE = 5
-MEDIUM_SIZE = 20
-BIGGER_SIZE = 50
-FIG_WIDTH = 40
-FIG_HEIGHT = 10
-
-plt.clf()
-
-plt.rc('figure', figsize=(8, 6))
-plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
-plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title
-plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
-plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
-plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
-plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize
-plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title
-plt.rc('figure', figsize=(6,4))
-
-g = sns.histplot(x="total_power_mean_watts", data=df_all_results, bins=25, fill=False)
-#g.ax.set_yscale('log')
-g.set_xlabel("Total Power (watts)")
-g.set_ylabel("Number of Jobs")
-plt.xticks(ticks=[0,250,500,750,1000,1250,1500], rotation=30)
-plt.tight_layout(pad=0)
-plt.savefig("./fig2a-distrib-job-power-mean.svg")
+# clear all Python memory
+import sys
+sys.modules[__name__].__dict__.clear()
+import gc
+gc.collect()
```
-### Max : Figure 2 (b)
-```{python}
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-TINY_SIZE = 2
-SMALL_SIZE = 5
-MEDIUM_SIZE = 20
-BIGGER_SIZE = 50
-FIG_WIDTH = 40
-FIG_HEIGHT = 10
-
-plt.clf()
-
-plt.rc('figure', figsize=(8, 6))
-plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes
-plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title
-plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
-plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
-plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels
-plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize
-plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title
-plt.rc('figure', figsize=(6,4))
-
-#g = sns.displot(x="total_power_max_watts", data=df_all_results)
-g = sns.histplot(x="total_power_max_watts", data=df_all_results, bins=25, fill=False)
-
-#g.ax.set_yscale('log')
-g.set_xlabel("Total Power (watts)")
-g.set_ylabel("Number of Jobs")
-plt.xticks(ticks=[0,250,500,750,1000,1250,1500,1750,2000], rotation=30)
-plt.tight_layout(pad=0)
-plt.savefig("./fig2b-distrib-job-power-max.svg")
+```{R}
+library(tidyverse)
+
+data_mean = read_csv('/tmp/allresults-mean.csv')
+data_mean %>% ggplot(aes(x=total_power_mean_watts)) +
+ geom_histogram() +
+ scale_y_continuous(labels = scales::label_number()) +
+ theme_bw(base_size=20) +
+ labs(
+ x='Total power (W)',
+ y='Number of jobs'
+ )
+ggsave('./fig2a-distrib-job-power-mean.pdf', width=6, height=3)
+ggsave('./fig2a-distrib-job-power-mean.svg', width=6, height=3)
+rm(data_mean)
+
+data_max = read_csv('/tmp/allresults-max.csv')
+data_max %>% ggplot(aes(x=total_power_max_watts)) +
+ geom_histogram() +
+ scale_y_continuous(labels = scales::label_number()) +
+ theme_bw(base_size=20) +
+ labs(
+ x='Total power (W)',
+ y='Number of jobs'
+ )
+ggsave('./fig2b-distrib-job-power-max.pdf', width=6, height=3)
+ggsave('./fig2b-distrib-job-power-max.svg', width=6, height=3)
+rm(data_max)
```
--
GitLab