qharlie

 ~/workspace  hg clone http://www.octave.org/hg/octave                                                                                      Mon 03 Apr 2017 10:43:17 AM CDT
real URL is http://hg.savannah.gnu.org/hgweb/octave
destination directory: octave
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added 23342 changesets with 155237 changes to 13201 files                                                                                                                   
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qharlie

import pandas as pd
import sys

loc = int(sys.argv[1])-1 if len(sys.argv) > 1 else -1
df = pd.read_csv("https://docs.google.com/spreadsheet/ccc?key=1xej5Nca2xUUtrZ1GCyPjFMqI9ZgNq_OhgnTxOOMQ2js&usp=sharing&output=csv")

class Keys:
    NAME="Paper Name"
    SUMMARY="High level summary"
    STATE="Does it claim a State of the Art result?"

qharlie

import pandas as pd
import sys

loc = int(sys.argv[1])-1 if len(sys.argv) > 1 else -1
df = pd.read_csv("Arxiv Paper Analysis Worksheet (Responses) - Form Responses 1.csv")

class Keys:
    NAME="Paper Name"
    SUMMARY="High level summary"
    STATE="Does it claim a State of the Art result?"

qharlie

values = {}
values['A1'] = [1]
values['A2'] = [2]
values['A3'] = [1]


box = 'A1'

for num in values[box]:
    values[box] = num

qharlie

# coding: utf-8

# ## COPY THIS FOR TRAINING DATA

# In[64]:

from sklearn.model_selection import train_test_split
import charba.api as capi
import pandas as pd

qharlie

features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.3,
                                                                            random_state=42)

clf = DecisionTreeClassifier(random_state=RANDOM_STATE)


param_grid = [
    {
        "pca__n_components": range(1, len(features_list) - 1, 1),
        "selectatmostkbest__k": [2, 3, 4, 5, 6, 'all'],

qharlie

for score in scores:
    sss = StratifiedShuffleSplit()
    search = GridSearchCV(pipe, param_grid, cv=sss, scoring="recall", n_jobs=-1)
    search.fit(features, labels)
    print("BEST SCORE = " + str(search.score(features_test,labels_test)))
    print("BEST PARAMS = " + str(search.best_params_))  # the parameter combination that together got the best f1 score
    print("BEST ESTIMATOR = " + str(search.best_estimator_))

qharlie

#!/usr/bin/python

import pickle
import numpy
from sklearn.feature_selection import SelectPercentile, f_classif

numpy.random.seed(42)

### The words (features) and authors (labels), already largely processed.
### These files should have been created from the previous (Lesson 10)

qharlie

# coding: utf-8

# ## COPY THIS FOR TRAINING DATA

# In[113]:

from sklearn.model_selection import train_test_split
import charba.api as capi
import pandas as pd

qharlie

FROM ubuntu:16.04
LABEL maintainer "Charlie"
LABEL com.nvidia.volumes.needed="nvidia_driver"

ENV DEBIAN_FRONTEND noninteractive

RUN NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
    NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
    apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub && \
    apt-key adv --export --no-emit-version -a $NVIDIA_GPGKEY_FPR | tail -n +5 > cudasign.pub && \