Merge pull request #546 from vanithakattumuri/main

#2 new the latex file results2Latex.py

PAMI/extras/graph/results2Latex.py

@@ -0,0 +1,167 @@
+# results2Latex is used to convert the given results into LatexFile.
+#
+# **Importing this algorithm into a python program**
+#
+#             from PAMI.extras.graph import results2Latex
+#
+#             obj = results2Latex()
+#
+#             obj.print(resultsDF, xaxis='minSup', yaxis='patterns', label='algorithm')
+#
+#             obj.print(resultsDF, xaxis='minSup', yaxis='runtime', label='algorithm')
+#
+#             obj.print(resultsDF, xaxis='minSup', yaxis='memoryRSS', label='algorithm')
+#
+#             obj.print(resultsDF, xaxis='minSup', yaxis='memoryUSS', label='algorithm')
+#
+#             obj.save(resultsDF, xaxis='minSup', yaxis='patterns', label='algorithm', oFile='patterns.tex')
+#
+#             obj.save(resultsDF, xaxis='minSup', yaxis='runtime', label='algorithm', oFile='runtime.tex')
+#
+#             obj.save(resultsDF, xaxis='minSup', yaxis='memoryRSS', label='algorithm', oFile='memoryRSS.tex')
+#
+#             obj.save(resultsDF, xaxis='minSup', yaxis='memoryUSS', label='algorithm', oFile='memoryUSS.tex')
+#
+
+__copyright__ = """
+Copyright (C)  2021 Rage Uday Kiran
+
+     This program is free software: you can redistribute it and/or modify
+     it under the terms of the GNU General Public License as published by
+     the Free Software Foundation, either version 3 of the License, or
+     (at your option) any later version.
+
+     This program is distributed in the hope that it will be useful,
+     but WITHOUT ANY WARRANTY; without even the implied warranty of
+     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+     GNU General Public License for more details.
+
+     You should have received a copy of the GNU General Public License
+     along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+
+
+import pandas as pd
+
+class results2Latex:
+    """
+    **About this algorithm**
+
+    :**Description**:  generateLatexCode is used to convert the given DataFrame into a LaTeX file.
+
+    :**Reference**:
+
+    **Attributes:** - **latexCode** (*str*) -- Stores the generated LaTeX code.
+
+    **Methods:**    - **generateLatexCode(result: pd.DataFrame, xColumn, yColumn, algorithmColumn=None)** -- *Generates LaTeX code based on DataFrame columns.*
+
+                    - **print(result: pd.DataFrame, xaxis, yaxis, label=None)** -- *Prints the LaTeX code.*
+
+                    - **save(result: pd.DataFrame, xaxis, yaxis, label=None, oFile="output.tex")** -- *Saves the LaTeX code to a .tex file.*
+
+    **Importing this algorithm into a Python program**
+
+    .. code-block:: python
+
+            from PAMI.extras.graph import results2Latex
+
+            resultsDF = Dataframe
+
+            obj = results2Latex()
+
+             obj.print(resultsDF, xaxis='minSup', yaxis='patterns', label='algorithm')
+
+             obj.print(resultsDF, xaxis='minSup', yaxis='runtime', label='algorithm')
+
+             obj.print(resultsDF, xaxis='minSup', yaxis='memoryRSS', label='algorithm')
+
+             obj.print(resultsDF, xaxis='minSup', yaxis='memoryUSS', label='algorithm')
+
+             obj.save(resultsDF, xaxis='minSup', yaxis='patterns', label='algorithm', oFile='patterns.tex')
+
+             obj.save(resultsDF, xaxis='minSup', yaxis='runtime', label='algorithm', oFile='runtime.tex')
+
+             obj.save(resultsDF, xaxis='minSup', yaxis='memoryRSS', label='algorithm', oFile='memoryRSS.tex')
+
+             obj.save(resultsDF, xaxis='minSup', yaxis='memoryUSS', label='algorithm', oFile='memoryUSS.tex')
+
+"""
+
+    def __init__(self):
+        self.latexCode = ""
+
+    def generateLatexCode(self, result: pd.DataFrame, xColumn, yColumn, algorithmColumn=None) -> None:
+        if result.empty:
+            raise ValueError("The input DataFrame is empty. Please provide a DataFrame with data.")
+
+        requiredColumns = [xColumn, yColumn]
+        if algorithmColumn:
+            requiredColumns.append(algorithmColumn)
+        for col in requiredColumns:
+            if col not in result.columns:
+                raise ValueError(f"Column '{col}' does not exist in the DataFrame.")
+
+        titles = [xColumn, yColumn]
+        legendary = pd.unique(result[algorithmColumn] if algorithmColumn else result.iloc[:, 0].values.ravel())
+        xaxisValues = result[xColumn].values
+        yaxisValues = result[yColumn].values
+        algo = result[algorithmColumn].values if algorithmColumn else result.iloc[:, 0].values
+        xLabel = titles[0]
+        colors = ['red', 'blue', 'green', 'black', 'yellow']
+        latexCode = ""
+        latexCode += "\\begin{axis}[\n\txlabel={\\Huge{" + xLabel + "}},"
+        latexCode += "\n\tylabel={\\Huge{" + titles[1] + "}},"
+        latexCode += "\n\txmin=" + str(min(xaxisValues)) + ", xmax=" + str(max(xaxisValues)) + ",]\n"
+
+        for num, legend in enumerate(legendary):
+            color = colors[num % len(colors)]
+            latexCode += "\\addplot+  [" + color + "]\n\tcoordinates {\n"
+            for x, y, a in zip(xaxisValues, yaxisValues, algo):
+                if legend == a:
+                    latexCode += f"({x},{y})\n"
+            latexCode += "\t};   \\addlegendentry{" + str(legend) + "}\n"
+        latexCode += "\\end{axis}"
+        self.latexCode = latexCode
+
+    def print(self, result: pd.DataFrame, xaxis, yaxis, label=None):
+        """
+        Print the LaTeX code with user-specified parameters.
+        """
+        self.generateLatexCode(result, xaxis, yaxis, label)
+        print(self.latexCode)
+
+    def save(self, result: pd.DataFrame, xaxis, yaxis, label=None, oFile="output.tex"):
+        """
+        Save the generated LaTeX code to a file with user-specified parameters.
+        """
+        self.generateLatexCode(result, xaxis, yaxis, label)
+        with open(oFile, "w") as latexWriter:
+            latexWriter.write(self.latexCode)
+        print(f"LaTeX file saved as {oFile}")
+
+
+
+"""data = {
+    'algorithm': ['Alg1', 'Alg2', 'Alg3', 'Alg4'],
+    'minSup': [200, 400, 600, 800],
+    'patterns': [800, 600, 400, 200],
+    'runtime': [107.7, 98.6, 86.8, 12.5],
+    'memoryRSS': [16, 15, 14, 13],
+    'memoryUSS': [26, 25, 24, 23]
+}
+resultsDF = pd.DataFrame(data)
+obj = results2Latex()
+
+obj.print(resultsDF, xaxis='minSup', yaxis='patterns', label='algorithm')
+obj.print(resultsDF, xaxis='minSup', yaxis='runtime', label='algorithm')
+obj.print(resultsDF, xaxis='minSup', yaxis='memoryRSS', label='algorithm')
+obj.print(resultsDF, xaxis='minSup', yaxis='memoryUSS', label='algorithm')
+
+obj.save(resultsDF, xaxis='minSup', yaxis='patterns', label='algorithm', oFile='patterns.tex')
+obj.save(resultsDF, xaxis='minSup', yaxis='runtime', label='algorithm', oFile='runtime.tex')
+obj.save(resultsDF, xaxis='minSup', yaxis='memoryRSS', label='algorithm', oFile='memoryRSS.tex')
+obj.save(resultsDF, xaxis='minSup', yaxis='memoryUSS', label='algorithm', oFile='memoryUSS.tex')"""
+
+
+