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imankulov/so_survey.ipynb

Created June 16, 2018 11:16
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so_survey.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"pd.set_option(\"display.max_rows\", 5000)\n",
"pd.set_option(\"display.max_columns\", 1000)\n",
"pd.set_option(\"display.max_colwidth\", 10000)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('survey_results_public.csv', low_memory=False)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"dummy_columns = [\n",
" 'Hobby',\n",
" 'OpenSource',\n",
" 'Country',\n",
" 'Student',\n",
" 'Employment',\n",
" 'FormalEducation',\n",
" 'UndergradMajor',\n",
" 'CompanySize',\n",
" 'YearsCoding',\n",
" 'YearsCodingProf',\n",
" 'JobSatisfaction', # !\n",
" 'CareerSatisfaction',\n",
" 'HopeFiveYears',\n",
" 'JobSearchStatus',\n",
" 'LastNewJob',\n",
" 'UpdateCV',\n",
" 'TimeFullyProductive',\n",
" 'TimeAfterBootcamp',\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.get_dummies(df[dummy_columns])"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train, test = train_test_split(df2, test_size = 0.2)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Hobby_No</th>\n",
" <th>Hobby_Yes</th>\n",
" <th>OpenSource_No</th>\n",
" <th>OpenSource_Yes</th>\n",
" <th>Country_Afghanistan</th>\n",
" <th>Country_Albania</th>\n",
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" <th>Country_Andorra</th>\n",
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" <th>Country_Chile</th>\n",
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" <th>Country_Ireland</th>\n",
" <th>Country_Israel</th>\n",
" <th>Country_Italy</th>\n",
" <th>Country_Jamaica</th>\n",
" <th>Country_Japan</th>\n",
" <th>Country_Jordan</th>\n",
" <th>Country_Kazakhstan</th>\n",
" <th>Country_Kenya</th>\n",
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" <th>Country_Kyrgyzstan</th>\n",
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" <th>Country_Mauritania</th>\n",
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" <th>Country_Micronesia, Federated States of...</th>\n",
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" <th>Country_Sweden</th>\n",
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" <th>Country_Syrian Arab Republic</th>\n",
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" <th>Country_Tajikistan</th>\n",
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" <th>Country_Trinidad and Tobago</th>\n",
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" <th>Country_Uganda</th>\n",
" <th>Country_Ukraine</th>\n",
" <th>Country_United Arab Emirates</th>\n",
" <th>Country_United Kingdom</th>\n",
" <th>Country_United Republic of Tanzania</th>\n",
" <th>Country_United States</th>\n",
" <th>Country_Uruguay</th>\n",
" <th>Country_Uzbekistan</th>\n",
" <th>Country_Venezuela, Bolivarian Republic of...</th>\n",
" <th>Country_Viet Nam</th>\n",
" <th>Country_Yemen</th>\n",
" <th>Country_Zambia</th>\n",
" <th>Country_Zimbabwe</th>\n",
" <th>Student_No</th>\n",
" <th>Student_Yes, full-time</th>\n",
" <th>Student_Yes, part-time</th>\n",
" <th>Employment_Employed full-time</th>\n",
" <th>Employment_Employed part-time</th>\n",
" <th>Employment_Independent contractor, freelancer, or self-employed</th>\n",
" <th>Employment_Not employed, and not looking for work</th>\n",
" <th>Employment_Not employed, but looking for work</th>\n",
" <th>Employment_Retired</th>\n",
" <th>FormalEducation_Associate degree</th>\n",
" <th>FormalEducation_Bachelor’s degree (BA, BS, B.Eng., etc.)</th>\n",
" <th>FormalEducation_I never completed any formal education</th>\n",
" <th>FormalEducation_Master’s degree (MA, MS, M.Eng., MBA, etc.)</th>\n",
" <th>FormalEducation_Other doctoral degree (Ph.D, Ed.D., etc.)</th>\n",
" <th>FormalEducation_Primary/elementary school</th>\n",
" <th>FormalEducation_Professional degree (JD, MD, etc.)</th>\n",
" <th>FormalEducation_Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)</th>\n",
" <th>FormalEducation_Some college/university study without earning a degree</th>\n",
" <th>UndergradMajor_A business discipline (ex. accounting, finance, marketing)</th>\n",
" <th>UndergradMajor_A health science (ex. nursing, pharmacy, radiology)</th>\n",
" <th>UndergradMajor_A humanities discipline (ex. literature, history, philosophy)</th>\n",
" <th>UndergradMajor_A natural science (ex. biology, chemistry, physics)</th>\n",
" <th>UndergradMajor_A social science (ex. anthropology, psychology, political science)</th>\n",
" <th>UndergradMajor_Another engineering discipline (ex. civil, electrical, mechanical)</th>\n",
" <th>UndergradMajor_Computer science, computer engineering, or software engineering</th>\n",
" <th>UndergradMajor_Fine arts or performing arts (ex. graphic design, music, studio art)</th>\n",
" <th>UndergradMajor_I never declared a major</th>\n",
" <th>UndergradMajor_Information systems, information technology, or system administration</th>\n",
" <th>UndergradMajor_Mathematics or statistics</th>\n",
" <th>UndergradMajor_Web development or web design</th>\n",
" <th>CompanySize_1,000 to 4,999 employees</th>\n",
" <th>CompanySize_10 to 19 employees</th>\n",
" <th>CompanySize_10,000 or more employees</th>\n",
" <th>CompanySize_100 to 499 employees</th>\n",
" <th>CompanySize_20 to 99 employees</th>\n",
" <th>CompanySize_5,000 to 9,999 employees</th>\n",
" <th>CompanySize_500 to 999 employees</th>\n",
" <th>CompanySize_Fewer than 10 employees</th>\n",
" <th>YearsCoding_0-2 years</th>\n",
" <th>YearsCoding_12-14 years</th>\n",
" <th>YearsCoding_15-17 years</th>\n",
" <th>YearsCoding_18-20 years</th>\n",
" <th>YearsCoding_21-23 years</th>\n",
" <th>YearsCoding_24-26 years</th>\n",
" <th>YearsCoding_27-29 years</th>\n",
" <th>YearsCoding_3-5 years</th>\n",
" <th>YearsCoding_30 or more years</th>\n",
" <th>YearsCoding_6-8 years</th>\n",
" <th>YearsCoding_9-11 years</th>\n",
" <th>YearsCodingProf_0-2 years</th>\n",
" <th>YearsCodingProf_12-14 years</th>\n",
" <th>YearsCodingProf_15-17 years</th>\n",
" <th>YearsCodingProf_18-20 years</th>\n",
" <th>YearsCodingProf_21-23 years</th>\n",
" <th>YearsCodingProf_24-26 years</th>\n",
" <th>YearsCodingProf_27-29 years</th>\n",
" <th>YearsCodingProf_3-5 years</th>\n",
" <th>YearsCodingProf_30 or more years</th>\n",
" <th>YearsCodingProf_6-8 years</th>\n",
" <th>YearsCodingProf_9-11 years</th>\n",
" <th>JobSatisfaction_Extremely dissatisfied</th>\n",
" <th>JobSatisfaction_Extremely satisfied</th>\n",
" <th>JobSatisfaction_Moderately dissatisfied</th>\n",
" <th>JobSatisfaction_Moderately satisfied</th>\n",
" <th>JobSatisfaction_Neither satisfied nor dissatisfied</th>\n",
" <th>JobSatisfaction_Slightly dissatisfied</th>\n",
" <th>JobSatisfaction_Slightly satisfied</th>\n",
" <th>CareerSatisfaction_Extremely dissatisfied</th>\n",
" <th>CareerSatisfaction_Extremely satisfied</th>\n",
" <th>CareerSatisfaction_Moderately dissatisfied</th>\n",
" <th>CareerSatisfaction_Moderately satisfied</th>\n",
" <th>CareerSatisfaction_Neither satisfied nor dissatisfied</th>\n",
" <th>CareerSatisfaction_Slightly dissatisfied</th>\n",
" <th>CareerSatisfaction_Slightly satisfied</th>\n",
" <th>HopeFiveYears_Doing the same work</th>\n",
" <th>HopeFiveYears_Retirement</th>\n",
" <th>HopeFiveYears_Working as a founder or co-founder of my own company</th>\n",
" <th>HopeFiveYears_Working as a product manager or project manager</th>\n",
" <th>HopeFiveYears_Working as an engineering manager or other functional manager</th>\n",
" <th>HopeFiveYears_Working in a career completely unrelated to software development</th>\n",
" <th>HopeFiveYears_Working in a different or more specialized technical role than the one I'm in now</th>\n",
" <th>JobSearchStatus_I am actively looking for a job</th>\n",
" <th>JobSearchStatus_I am not interested in new job opportunities</th>\n",
" <th>JobSearchStatus_I’m not actively looking, but I am open to new opportunities</th>\n",
" <th>LastNewJob_Between 1 and 2 years ago</th>\n",
" <th>LastNewJob_Between 2 and 4 years ago</th>\n",
" <th>LastNewJob_I've never had a job</th>\n",
" <th>LastNewJob_Less than a year ago</th>\n",
" <th>LastNewJob_More than 4 years ago</th>\n",
" <th>UpdateCV_A friend told me about a job opportunity</th>\n",
" <th>UpdateCV_A recruiter contacted me</th>\n",
" <th>UpdateCV_I did not receive an expected change in compensation</th>\n",
" <th>UpdateCV_I had a negative experience or interaction at work</th>\n",
" <th>UpdateCV_I received bad news about the future of my company or department</th>\n",
" <th>UpdateCV_I received negative feedback on my job performance</th>\n",
" <th>UpdateCV_I saw an employer’s advertisement</th>\n",
" <th>UpdateCV_My job status or other personal status changed</th>\n",
" <th>TimeFullyProductive_Less than a month</th>\n",
" <th>TimeFullyProductive_More than a year</th>\n",
" <th>TimeFullyProductive_Nine months to a year</th>\n",
" <th>TimeFullyProductive_One to three months</th>\n",
" <th>TimeFullyProductive_Six to nine months</th>\n",
" <th>TimeFullyProductive_Three to six months</th>\n",
" <th>TimeAfterBootcamp_Four to six months</th>\n",
" <th>TimeAfterBootcamp_I already had a full-time job as a developer when I began the program</th>\n",
" <th>TimeAfterBootcamp_I haven’t gotten a developer job</th>\n",
" <th>TimeAfterBootcamp_Immediately after graduating</th>\n",
" <th>TimeAfterBootcamp_Less than a month</th>\n",
" <th>TimeAfterBootcamp_Longer than a year</th>\n",
" <th>TimeAfterBootcamp_One to three months</th>\n",
" <th>TimeAfterBootcamp_Six months to a year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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"</div>"
],
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"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSatisfaction_Extremely satisfied \\\n",
"64269 0 \n",
"43968 1 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSatisfaction_Moderately dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSatisfaction_Moderately satisfied \\\n",
"64269 1 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSatisfaction_Neither satisfied nor dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSatisfaction_Slightly dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 1 \n",
"87164 0 \n",
"\n",
" JobSatisfaction_Slightly satisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" CareerSatisfaction_Extremely dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" CareerSatisfaction_Extremely satisfied \\\n",
"64269 1 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" CareerSatisfaction_Moderately dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 1 \n",
"\n",
" CareerSatisfaction_Moderately satisfied \\\n",
"64269 0 \n",
"43968 1 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" CareerSatisfaction_Neither satisfied nor dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" CareerSatisfaction_Slightly dissatisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 1 \n",
"87164 0 \n",
"\n",
" CareerSatisfaction_Slightly satisfied \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" HopeFiveYears_Doing the same work HopeFiveYears_Retirement \\\n",
"64269 0 0 \n",
"43968 0 0 \n",
"93093 0 0 \n",
"10584 0 0 \n",
"87164 0 0 \n",
"\n",
" HopeFiveYears_Working as a founder or co-founder of my own company \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" HopeFiveYears_Working as a product manager or project manager \\\n",
"64269 1 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" HopeFiveYears_Working as an engineering manager or other functional manager \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" HopeFiveYears_Working in a career completely unrelated to software development \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" HopeFiveYears_Working in a different or more specialized technical role than the one I'm in now \\\n",
"64269 0 \n",
"43968 1 \n",
"93093 0 \n",
"10584 1 \n",
"87164 1 \n",
"\n",
" JobSearchStatus_I am actively looking for a job \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSearchStatus_I am not interested in new job opportunities \\\n",
"64269 0 \n",
"43968 1 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" JobSearchStatus_I’m not actively looking, but I am open to new opportunities \\\n",
"64269 1 \n",
"43968 0 \n",
"93093 0 \n",
"10584 1 \n",
"87164 1 \n",
"\n",
" LastNewJob_Between 1 and 2 years ago \\\n",
"64269 1 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" LastNewJob_Between 2 and 4 years ago LastNewJob_I've never had a job \\\n",
"64269 0 0 \n",
"43968 0 0 \n",
"93093 0 0 \n",
"10584 0 0 \n",
"87164 1 0 \n",
"\n",
" LastNewJob_Less than a year ago LastNewJob_More than 4 years ago \\\n",
"64269 0 0 \n",
"43968 1 0 \n",
"93093 0 0 \n",
"10584 0 1 \n",
"87164 0 0 \n",
"\n",
" UpdateCV_A friend told me about a job opportunity \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 1 \n",
"\n",
" UpdateCV_A recruiter contacted me \\\n",
"64269 1 \n",
"43968 1 \n",
"93093 0 \n",
"10584 1 \n",
"87164 0 \n",
"\n",
" UpdateCV_I did not receive an expected change in compensation \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" UpdateCV_I had a negative experience or interaction at work \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" UpdateCV_I received bad news about the future of my company or department \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" UpdateCV_I received negative feedback on my job performance \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" UpdateCV_I saw an employer’s advertisement \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" UpdateCV_My job status or other personal status changed \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeFullyProductive_Less than a month \\\n",
"64269 1 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeFullyProductive_More than a year \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeFullyProductive_Nine months to a year \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeFullyProductive_One to three months \\\n",
"64269 0 \n",
"43968 1 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeFullyProductive_Six to nine months \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeFullyProductive_Three to six months \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 1 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_Four to six months \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_I already had a full-time job as a developer when I began the program \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_I haven’t gotten a developer job \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_Immediately after graduating \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_Less than a month \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_Longer than a year \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_One to three months \\\n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 \n",
"\n",
" TimeAfterBootcamp_Six months to a year \n",
"64269 0 \n",
"43968 0 \n",
"93093 0 \n",
"10584 0 \n",
"87164 0 "
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/roman/workspace/keras-playground/env/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"from sklearn.cross_validation import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Extremely satisfied', 'Moderately dissatisfied',\n",
" 'Moderately satisfied', 'Neither satisfied nor dissatisfied',\n",
" 'Slightly satisfied', nan, 'Slightly dissatisfied',\n",
" 'Extremely dissatisfied'], dtype=object)"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.JobSatisfaction.unique()"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [],
"source": [
"satistaction_levels = {\n",
" 'Extremely satisfied': 0.99,\n",
" 'Moderately satisfied': .8,\n",
" 'Slightly satisfied': .6,\n",
" 'Neither satisfied nor dissatisfied': 0.5,\n",
" 'Slightly dissatisfied': 0.4,\n",
" 'Moderately dissatisfied': 0.2,\n",
" 'Extremely dissatisfied': 0,\n",
" np.nan: np.nan,\n",
"}\n",
"y = df.JobSatisfaction.apply(lambda v: satistaction_levels[v]).dropna()"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [],
"source": [
"X = df2.iloc[y.index]"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"55420/55420 [==============================] - 2s 32us/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"55420/55420 [==============================] - 2s 32us/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"55420/55420 [==============================] - 2s 32us/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"55420/55420 [==============================] - 2s 33us/step - loss: 0.0000e+00 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"55420/55420 [==============================] - 2s 37us/step - loss: 0.0000e+00 - acc: 0.0000e+00\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11ad00c50>"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(units=64, activation='relu', input_dim=X_train.shape[1]))\n",
"model.add(Dense(units=1, activation='softmax'))\n",
"\n",
"model.compile(loss='sparse_categorical_crossentropy',\n",
" optimizer='sgd',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X_train.values, y_train.values, epochs=5, batch_size=32)"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13856/13856 [==============================] - 0s 9us/step\n"
]
}
],
"source": [
"loss_and_metrics = model.evaluate(X_test, y_test, batch_size=128)"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.0, 0.0]"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss_and_metrics"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mSignature:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m\n",
"Returns the loss value & metrics values for the model in test mode.\n",
"\n",
"Computation is done in batches.\n",
"\n",
"# Arguments\n",
" x: Numpy array of test data (if the model has a single input),\n",
" or list of Numpy arrays (if the model has multiple inputs).\n",
" If input layers in the model are named, you can also pass a\n",
" dictionary mapping input names to Numpy arrays.\n",
" `x` can be `None` (default) if feeding from\n",
" framework-native tensors (e.g. TensorFlow data tensors).\n",
" y: Numpy array of target (label) data\n",
" (if the model has a single output),\n",
" or list of Numpy arrays (if the model has multiple outputs).\n",
" If output layers in the model are named, you can also pass a\n",
" dictionary mapping output names to Numpy arrays.\n",
" `y` can be `None` (default) if feeding from\n",
" framework-native tensors (e.g. TensorFlow data tensors).\n",
" batch_size: Integer or `None`.\n",
" Number of samples per evaluation step.\n",
" If unspecified, `batch_size` will default to 32.\n",
" verbose: 0 or 1. Verbosity mode.\n",
" 0 = silent, 1 = progress bar.\n",
" sample_weight: Optional Numpy array of weights for\n",
" the test samples, used for weighting the loss function.\n",
" You can either pass a flat (1D)\n",
" Numpy array with the same length as the input samples\n",
" (1:1 mapping between weights and samples),\n",
" or in the case of temporal data,\n",
" you can pass a 2D array with shape\n",
" `(samples, sequence_length)`,\n",
" to apply a different weight to every timestep of every sample.\n",
" In this case you should make sure to specify\n",
" `sample_weight_mode=\"temporal\"` in `compile()`.\n",
" steps: Integer or `None`.\n",
" Total number of steps (batches of samples)\n",
" before declaring the evaluation round finished.\n",
" Ignored with the default value of `None`.\n",
"\n",
"# Returns\n",
" Scalar test loss (if the model has a single output and no metrics)\n",
" or list of scalars (if the model has multiple outputs\n",
" and/or metrics). The attribute `model.metrics_names` will give you\n",
" the display labels for the scalar outputs.\n",
"\u001b[0;31mFile:\u001b[0m ~/workspace/keras-playground/env/lib/python3.6/site-packages/keras/engine/training.py\n",
"\u001b[0;31mType:\u001b[0m method\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"?model.evaluate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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