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Merge pull request #4 from demarley/hepPlotter-update
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HEP plotter update
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@demarley
demarley authored Oct 24, 2018
2 parents cc722d4 + c7cfa75 commit 40d454b
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -17,7 +17,7 @@ Clone the repository and the hepPlotter repository (Asimov uses hepPlotter to ma
git clone https://github.com/demarley/asimov.git
git clone https://github.com/demarley/hepPlotter.git
cd hepPlotter/
git checkout tags/v0.3 # current compatibility
git checkout tags/v0.4.2 # current compatibility
```

Please see the `examples/` directory for an example on using this framework with data from the Higgs Boson Machine Learning Challenge.
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97 changes: 34 additions & 63 deletions examples/0-simple.ipynb
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Expand Up @@ -23,9 +23,7 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"import os\n",
Expand All @@ -44,9 +42,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"cwd = os.getcwd()\n",
Expand All @@ -63,22 +59,12 @@
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/demarley/Desktop/CERN/CMS/common/asimov/examples /Users/demarley/Desktop/CERN/CMS/common/hepPlotter/python//examples\n",
"Welcome to JupyROOT 6.10/02\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/demarley/anaconda2/lib/python2.7/site-packages/matplotlib/style/core.py:51: UserWarning: Style includes a parameter, 'backend', that is not related to style. Ignoring\n",
" \"to style. Ignoring\".format(key))\n",
"/Users/demarley/root_build/lib/ROOT.py:318: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" return _orig_ihook( name, *args, **kwds )\n",
"Using TensorFlow backend.\n"
]
}
Expand Down Expand Up @@ -118,7 +104,7 @@
"WARNING :: CONFIG : Using default value.\n",
"WARNING :: CONFIG : The configuration file does not contain dnn_data\n",
"WARNING :: CONFIG : Using default value.\n",
" INFO :: RUN : Saving output to ./example/training-19Sep2018-1402/\n"
" INFO :: RUN : Saving output to ./example/training-23Oct2018-2325/\n"
]
}
],
Expand Down Expand Up @@ -150,9 +136,7 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"dnn = Training() # class to do the training"
Expand All @@ -161,14 +145,13 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"dnn.variable_labels = plb.variable_labels() # labels for variables\n",
"dnn.sample_labels = plb.sample_labels() # labels for samples\n",
"\n",
"dnn.backend = 'uproot' # Set the backend for hepPlotter\n",
"dnn.hep_data = config.hep_data\n",
"dnn.model_name = config.dnn_data\n",
"dnn.msg_svc = vb\n",
Expand Down Expand Up @@ -221,13 +204,15 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/demarley/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
"/Users/demarley/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n",
"/Users/demarley/Desktop/CERN/CMS/common/hepPlotter/python/histogram1D.py:236: RuntimeWarning: divide by zero encountered in divide\n",
"/Users/demarley/Desktop/Home/hepex/hepPlotter/python/histogram1D.py:236: RuntimeWarning: divide by zero encountered in divide\n",
" ratio_data.data.content = (num_data / np.sqrt(den_data)).copy()\n",
"/Users/demarley/Desktop/CERN/CMS/common/hepPlotter/python/histogram1D.py:236: RuntimeWarning: invalid value encountered in divide\n",
"/Users/demarley/Desktop/Home/hepex/hepPlotter/python/histogram1D.py:236: RuntimeWarning: invalid value encountered in divide\n",
" ratio_data.data.content = (num_data / np.sqrt(den_data)).copy()\n",
"/Users/demarley/Desktop/CERN/CMS/common/asimov/python/util.py:112: RuntimeWarning: invalid value encountered in divide\n",
"/Users/demarley/Desktop/Home/hepex/asimov/python/util.py:112: RuntimeWarning: invalid value encountered in divide\n",
" tmp = np.divide( (sig-bkg)**2 , (sig+bkg), dtype=np.float32)\n",
"/Users/demarley/Desktop/Home/hepex/asimov/python/util.py:112: RuntimeWarning: divide by zero encountered in divide\n",
" tmp = np.divide( (sig-bkg)**2 , (sig+bkg), dtype=np.float32)\n"
]
},
Expand All @@ -239,39 +224,29 @@
" INFO :: FOUNDATION : -- pre-training :: separations\n",
"Train on 293538 samples, validate on 97846 samples\n",
"Epoch 1/10\n",
"293538/293538 [==============================] - 9s - loss: 0.5018 - acc: 0.7537 - val_loss: 0.4736 - val_acc: 0.7731\n",
"293538/293538 [==============================] - 30s 101us/step - loss: 0.5009 - acc: 0.7547 - val_loss: 0.4798 - val_acc: 0.7717\n",
"Epoch 2/10\n",
"293538/293538 [==============================] - 8s - loss: 0.4621 - acc: 0.7815 - val_loss: 0.4504 - val_acc: 0.7884\n",
"293538/293538 [==============================] - 26s 89us/step - loss: 0.4588 - acc: 0.7850 - val_loss: 0.4451 - val_acc: 0.7923\n",
"Epoch 3/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4398 - acc: 0.7958 - val_loss: 0.4311 - val_acc: 0.8016\n",
"293538/293538 [==============================] - 26s 89us/step - loss: 0.4381 - acc: 0.7971 - val_loss: 0.4308 - val_acc: 0.8026\n",
"Epoch 4/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4300 - acc: 0.8011 - val_loss: 0.4214 - val_acc: 0.8069\n",
"293538/293538 [==============================] - 26s 89us/step - loss: 0.4306 - acc: 0.8014 - val_loss: 0.4204 - val_acc: 0.8071\n",
"Epoch 5/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4260 - acc: 0.8027 - val_loss: 0.4300 - val_acc: 0.7984\n",
"293538/293538 [==============================] - 26s 89us/step - loss: 0.4267 - acc: 0.8025 - val_loss: 0.4363 - val_acc: 0.7960\n",
"Epoch 6/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4226 - acc: 0.8044 - val_loss: 0.4203 - val_acc: 0.8045\n",
"293538/293538 [==============================] - 26s 89us/step - loss: 0.4230 - acc: 0.8042 - val_loss: 0.4269 - val_acc: 0.8032\n",
"Epoch 7/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4208 - acc: 0.8055 - val_loss: 0.4189 - val_acc: 0.8071\n",
"293538/293538 [==============================] - 27s 92us/step - loss: 0.4213 - acc: 0.8053 - val_loss: 0.4222 - val_acc: 0.8030\n",
"Epoch 8/10\n",
"293538/293538 [==============================] - 11s - loss: 0.4189 - acc: 0.8062 - val_loss: 0.4131 - val_acc: 0.8096\n",
"293538/293538 [==============================] - 26s 90us/step - loss: 0.4193 - acc: 0.8058 - val_loss: 0.4194 - val_acc: 0.8071\n",
"Epoch 9/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4172 - acc: 0.8075 - val_loss: 0.4177 - val_acc: 0.8074\n",
"293538/293538 [==============================] - 25s 86us/step - loss: 0.4181 - acc: 0.8070 - val_loss: 0.4223 - val_acc: 0.8056\n",
"Epoch 10/10\n",
"293538/293538 [==============================] - 9s - loss: 0.4154 - acc: 0.8084 - val_loss: 0.4238 - val_acc: 0.8050\n",
" INFO :: DL : Plot the train/test predictions\n",
"293538/293538 [==============================] - 26s 87us/step - loss: 0.4160 - acc: 0.8078 - val_loss: 0.4160 - val_acc: 0.8092\n",
" INFO :: TRAINING : Plot the train/test predictions\n",
" INFO :: FOUNDATION : -- post-training :: ROC\n",
" INFO :: FOUNDATION : -- post-training :: History\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-09-19 14:04:16.511819: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.\n",
"2018-09-19 14:04:16.511837: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.\n",
"2018-09-19 14:04:16.511842: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.\n",
"2018-09-19 14:04:16.511848: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.\n"
]
}
],
"source": [
Expand All @@ -297,9 +272,7 @@
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import IFrame"
Expand All @@ -317,14 +290,14 @@
" <iframe\n",
" width=\"600\"\n",
" height=\"300\"\n",
" src=\"./example/training-19Sep2018-1402/acc_epochs.pdf\"\n",
" src=\"./example/training-23Oct2018-2325/acc_epochs.pdf\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x1c37ac1150>"
"<IPython.lib.display.IFrame at 0x1a3bb66dd0>"
]
},
"execution_count": 9,
Expand All @@ -348,14 +321,14 @@
" <iframe\n",
" width=\"600\"\n",
" height=\"300\"\n",
" src=\"./example/training-19Sep2018-1402/loss_epochs.pdf\"\n",
" src=\"./example/training-23Oct2018-2325/loss_epochs.pdf\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x1c3a816050>"
"<IPython.lib.display.IFrame at 0x1096eeb90>"
]
},
"execution_count": 10,
Expand All @@ -379,14 +352,14 @@
" <iframe\n",
" width=\"600\"\n",
" height=\"300\"\n",
" src=\"./example/training-19Sep2018-1402/roc_curve.pdf\"\n",
" src=\"./example/training-23Oct2018-2325/roc_curve.pdf\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x1c3e187850>"
"<IPython.lib.display.IFrame at 0x1a3bb2cfd0>"
]
},
"execution_count": 11,
Expand All @@ -410,14 +383,14 @@
" <iframe\n",
" width=\"600\"\n",
" height=\"300\"\n",
" src=\"./example/training-19Sep2018-1402/hist_DNN_prediction.pdf\"\n",
" src=\"./example/training-23Oct2018-2325/hist_DNN_prediction.pdf\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x1c3b6cb410>"
"<IPython.lib.display.IFrame at 0x1a30f69090>"
]
},
"execution_count": 12,
Expand All @@ -432,9 +405,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": []
}
Expand All @@ -455,7 +426,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
"version": "2.7.15"
}
},
"nbformat": 4,
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11 changes: 8 additions & 3 deletions python/empire.py
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Expand Up @@ -49,6 +49,7 @@ def __init__(self):
self.sample_labels = {} # Formatted sample labels
self.variable_labels = {} # Formatted variable labels

self.backend = 'uproot' # backend for hepPlotter
self.msg_svc = util.VERBOSE() # 'level' for printing statements
self.output_dir = '' # directory/path to store the plots
self.image_format = 'pdf' # figure format (PDF matches with backend!)
Expand Down Expand Up @@ -103,6 +104,7 @@ def feature(self,dataframe,ndims=-1):

hist = Histogram1D()

hist.backend = self.backend
hist.normed = True
hist.stacked = False
hist.binning = vl.binning
Expand Down Expand Up @@ -181,6 +183,7 @@ def feature(self,dataframe,ndims=-1):

hist = Histogram2D()

hist.backend = self.backend
hist.colormap = 'default'
hist.colorbar['title'] = "Events"

Expand Down Expand Up @@ -304,8 +307,8 @@ def separation(self):
separations = [self.separations['-'.join(f)]['-'.join(target)] for f in listOfFeaturePairs]

# Now repeat the entries with flipped indices to get the full matrix
x = list(x_coord)+list(y_coord)
y = list(y_coord)+list(x_coord)
x = np.asarray(list(x_coord)+list(y_coord))
y = np.asarray(list(y_coord)+list(x_coord))
separations += separations

# make the plot
Expand All @@ -314,6 +317,7 @@ def separation(self):
hist.colormap = 'default'
hist.colorbar['title'] = "Separation"

hist.backend = self.backend
hist.x_label = "{0} - {1}".format(target_a_label,target_b_label)
hist.y_label = ''
hist.binning = [range(nfeatures+1),range(nfeatures+1)]
Expand All @@ -323,7 +327,7 @@ def separation(self):
hist.CMSlabelStatus = self.CMSlabelStatus

hist.initialize()
hist.Add([x,y],weights=separations,name='-'.join(target))
hist.Add([x,y],weights=np.asarray(separations),name='-'.join(target))

fig = hist.execute()

Expand Down Expand Up @@ -414,6 +418,7 @@ def _prediction(self,train_data={},test_data={},c=None):

hist = Histogram1D()

hist.backend = self.backend
hist.normed = True # compare shape differences (likely don't have the same event yield)
hist.format = self.image_format
hist.saveAs = "{0}/hist_DNN_prediction{1}".format(self.output_dir,target_name)
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2 changes: 2 additions & 0 deletions python/foundation.py
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Expand Up @@ -70,6 +70,7 @@ def __init__(self):
self.runDiagnostics = False # Make plots pre/post training
self.msg_svc = None
self.verbose = True
self.backend = 'uproot' # backend for making plots with hepPlotter
self.equal_statistics = True # Equal statistics for each class in the df


Expand All @@ -92,6 +93,7 @@ def initialize(self):

## -- Plotting framework
self.plotter = Empire() # class for plotting relevant NN information
self.plotter.backend = self.backend
self.plotter.output_dir = self.output_dir
self.plotter.image_format = 'pdf'
self.plotter.features = self.features
Expand Down

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