def plotpoints(): whale = Whale() params = default_params() params['depth'] = g('depth', 0) params['period'] = g('period', None) params['flot_time'] = True return whale.plotpoints(**params) '''
def plotpoints(): whale = Whale() params = default_params() params['depth'] = g('depth', 0) params['period'] = g('period', None) params['sort'] = g('sort', None) params['limit'] = g('limit', 10) params['tzoffset'] = g('tzoffset', 0.0) params['flot_time'] = True return whale.plotpoints(**params)
def graph(): whale = Whale() points = whale.plotpoints(**default_params()) params = {'script_tag': util.JS_TAG, 'flotpoints': json.dumps(points), 'random_name': 'graph_psuedorandom', } return """ <div id="%(random_name)s" style="width:97%%;height:97%%;"> </div> %(script_tag)s <script type="text/javascript"> dimensions = %(flotpoints)s; first_dimension = get_keys(dimensions)[0]; first_metric = get_keys(dimensions[first_dimension])[0]; //data = dimensions['[\"empty\"]']['hits']; data = dimensions[first_dimension][first_metric]; $.plot($("#%(random_name)s"), [ {data: data, lines: {show: true}}, ], { xaxis: { mode: "time" } }); </script>"""%params
def graph(): from periods import Period params = {'pk': g('pk', '_', False), 'dimension': g('dimension', '_', False), 'metric': g('metric', 'hits', False), 'depth': g('depth', 0), 'tzoffset': g('tzoffset', 0.0), 'period': g('period', str(Period.get(None))), 'area': g('area', ''), } pk = params['pk'] dimension = params['dimension'] metric = params['metric'] period = Period.get(params['period']) debug = g('debug', False) parent_div = g('parent_div', 'hailwhale_graphs') table = g('table', False) height = g('height', '300px') params['title'] = g('title', '') if not params['title']: pkname = g('pk', '') dimname = util.try_loads(g('dimension', 'Overall')) dimname = isinstance(dimname, list) and dimname[-1] or dimname params['title'] = '%s [%s]' % (util.maybe_dumps(pkname), util.maybe_dumps(dimname)) if isinstance(table, basestring): table = table.lower() == 'true' hwurl = req.GET.get('hwurl', req.url.split('graph.js')[0]) params['autoupdate'] = g('live', True) params['interval'] = g('interval', 6000) graph_id = hashlib.md5(str(params)).hexdigest() include_string = \ "document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/jquery.min.js'></script>\");"%hwurl if table: try: columns = int(g('table', 6, int)) except: columns = 6 pps = Whale.plotpoints(pk, dimension, metric, period=period, depth=params['depth']) dates = [p for p in Period.get(period).datetimes_strs()][(-1*columns - 1):] table_str = ''' $('#{id} .table').html('<table style="width: 100%"> <tr> <th></th> <th></th> {columns} </tr> '''.strip().format(id=graph_id,columns=' '.join([ '<th>%s</th>'%date.replace('00:00:00 ', '') for date in dates])) dimensions = pps.keys() if '_' in dimensions: dimensions.remove('_') dimensions = ['_'] + dimensions for dimension_counter, dimension in enumerate(dimensions): checked = 'off' if dimension_counter < 10: checked = 'on' if dimension == '_': if params['depth']: continue dimension_name = '<b>Overall</b>' else: dimension_name = dimension.capitalize() table_str += ''' <tr> <td><input id="" style="display: none" type="checkbox" value="{checked}" name="checkbox-{pk}-{dimension}"></td> <td>{dimension_name}</td> {columns} </tr> '''.format(pk=pk, dimension=dimension, checked=checked, dimension_name=dimension_name, columns=' '.join([ "<td>%s</td>"%int(pps[dimension][metric][date]) for date in dates])).strip() table_str += '''</table>');''' else: table_str = '' include_string = \ "document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/hailwhale.min.js'></script>\");"%hwurl return_string = ''' appended=false;\n document.write('<div id="{id}"><div class="graph" style="height: {height}"></div><div class="table"></div></div>');\n function jqinit() {{\n if(typeof(jQuery) == 'undefined' || typeof(jQuery.hailwhale) == 'undefined') {{\n if(!appended) {{\n appended = true;\n {include_string}\n }}\n setTimeout(jqinit, 250);\n }} else {{\n $(function() {{\n $.hailwhale('{hwurl}').add_graph('{id} .graph', {options});\n {table_str} }});\n }} }} jqinit();\n '''.format(parent_div=parent_div, include_string=include_string, hwurl=hwurl, table_str=table_str, height=height, id=graph_id, options=util.maybe_dumps(params)) return return_string
class TestHailWhale(unittest.TestCase): def setUp(self): from hail import Hail from whale import Whale self.hail = Hail() self.whale = Whale() def testGetSubdimensions(self): t = 'subs_%s' % str(time.time()) self.whale.count_now(t, {'a': 1, 'b': 2}) subs = self.whale.get_subdimensions(t) assert('a' in subs) assert('b' in subs) def testGetAllSubdimensions(self): t = 'all_subs_%s' % str(time.time()) self.whale.count_now(t, {'a': 1, 'b': 2}) subs = self.whale.all_subdimensions(t) assert('a' in subs) assert(['a', '1'] in subs) assert('b' in subs) assert(['b', '2'] in subs) def testPlotpoints(self): t = str(time.time()) for i in range(5): self.whale.count_now('test_plotpoints', t, {'hits': 1, 'values': 5}) plotpoints = self.whale.plotpoints('test_plotpoints', t, ['hits', 'values'], points_type=list) self.assertEqual(plotpoints[t]['hits'][-1][1], 5) self.assertEqual(plotpoints[t]['values'][-1][1], 25) def testPlotpointsDepth(self): t = str(time.time()) self.whale.count_now('test_depth', {t: 'a'}) self.whale.count_now('test_depth', {t: 'b'}) self.whale.count_now('test_depth', {t: 'b'}) self.whale.count_now('test_depth', {t: {'c': 'child'}}) # Test 1 level deep plotpoints = self.whale.plotpoints('test_depth', t, points_type=list, depth=1) self.assertEqual(plotpoints[maybe_dumps([t, 'a'])]['hits'][-1][1], 1) self.assertEqual(plotpoints[maybe_dumps([t, 'b'])]['hits'][-1][1], 2) self.assertEqual(plotpoints[maybe_dumps([t, 'c'])]['hits'][-1][1], 1) self.assertEqual(False, maybe_dumps([t, 'c', 'child']) in plotpoints) # Test 2 levels deep plotpoints = self.whale.plotpoints('test_depth', t, points_type=list, depth=2) self.assertEqual(True, maybe_dumps([t, 'c', 'child']) in plotpoints) self.assertEqual(plotpoints[maybe_dumps([t, 'c', 'child'])]['hits'][-1][1], 1) # Test ranking and limiting plotpoints = self.whale.plotpoints('test_depth', t, points_type=list, depth=1, limit=2) self.assertEqual(plotpoints[maybe_dumps([t, 'b'])]['hits'][-1][1], 2) self.assertEqual(True, maybe_dumps([t, 'a']) not in plotpoints) self.assertEqual(True, maybe_dumps([t, 'c']) not in plotpoints) def testRatioPlotpoints(self): t = str(time.time()) for i in range(5): self.whale.count_now('test_ratio', t, {'hit': 1, 'value': 5}) plotpoints = self.whale.plotpoints('test_ratio', t, ['hit', 'value', 'value/hit'], points_type=list) self.assertEqual(plotpoints[t]['hit'][-1][1], 5) self.assertEqual(plotpoints[t]['value'][-1][1], 25) self.assertEqual(plotpoints[t]['value/hit'][-1][1], 5) def testRankSubdimensionsScalar(self): t = str(time.time()) self.whale.count_now('test_rank', [t, 'a', 'asub1'], {'value': 1}) self.whale.count_now('test_rank', [t, 'a', 'asub2'], {'value': 30}) self.whale.count_now('test_rank', [t, 'b'], {'value': 80}) self.whale.count_now('test_rank', [t, 'c'], {'value': 10}) ranked = self.whale.rank_subdimensions_scalar('test_rank', t, 'value') self.assertEqual(ranked[maybe_dumps([t, 'a'])]['important'], False) self.assertEqual(ranked[maybe_dumps([t, 'a', 'asub1'])]['important'], False) self.assertEqual(ranked[maybe_dumps([t, 'a', 'asub2'])]['important'], True) self.assertEqual(ranked[maybe_dumps([t, 'b'])]['important'], True) self.assertEqual(ranked[maybe_dumps([t, 'c'])]['important'], False) def testRankSubdimensionsRatio(self): t = str(time.time()) pk = 'test_ratio_rank' # OVERALL STATS: 529,994 value, 50,000 visitors, 10.6 value per visitor # Not important, too close to overall self.whale.count_now(pk, [t, 'a', 'asub1'], {'value': 54989, 'visitors': 4999}) # 11 value per visitor # Important, high relative ratio self.whale.count_now(pk, [t, 'a', 'asub2'], {'value': 375000, 'visitors': 25000}) # 15 value per visitor # Important, low relative ratio self.whale.count_now(pk, [t, 'b'], {'value': 100000, 'visitors': 20000}) # 5 value per visitor # Not important, not enough visitors self.whale.count_now(pk, [t, 'c'], {'value': 5, 'visitors': 1}) # 5 value per visitor one_level = self.whale.rank_subdimensions_ratio('test_rank_ratio', 'value', 'visitors', t, recursive=False) all_levels = self.whale.rank_subdimensions_ratio(pk, 'value', 'visitors', t) self.assertEqual(True, maybe_dumps([t, 'a', 'asub1']) not in one_level) self.assertEqual(all_levels[maybe_dumps([t, 'a', 'asub1'])]['important'], False) self.assertEqual(all_levels[maybe_dumps([t, 'a', 'asub2'])]['important'], True) self.assertEqual(all_levels[maybe_dumps([t, 'b'])]['important'], True) self.assertEqual(all_levels[maybe_dumps([t, 'c'])]['important'], False) def testBasicDecision(self): pk = 'test_basic_decision' decision = str(time.time()) # Make a decision, any decision, from no information whatsoever good, bad, test = self.whale.weighted_reasons(pk, 'random', [1,2,3]) #_print_reasons(good, bad, test) any_one = self.whale.decide_from_reasons(good, bad, test) self.assertEqual(True, any_one in [1, 2, 3]) # OK, now how about something somewhat informed? # This will be easy. Slogan A makes us huge profit. Products B and C suck. # D looks promissing but isn't yet significant opts = ['a', 'b', 'c', 'd'] self.whale.count_now([pk, decision, 'a'], None, dict(dollars=5000, visitors=1000)) self.whale.count_now([pk, decision, 'b'], None, dict(dollars=0, visitors=2000)) self.whale.count_now([pk, decision, 'c'], None, dict(dollars=0, visitors=2000)) self.whale.count_now([pk, decision, 'd'], None, dict(dollars=50, visitors=10)) good, bad, test = self.whale.weighted_reasons(pk, decision, opts, formula='dollars/visitors') #_print_reasons(good, bad, test) self.assertEqual(True, 'a' in good.keys()) self.assertEqual(True, 'b' in bad.keys()) self.assertEqual(True, 'c' in bad.keys()) self.assertEqual(True, 'd' in test.keys()) which_one = self.whale.decide(pk, decision, opts, formula='dollars/visitors', bad_idea_threshold=0, test_idea_threshold=0) self.assertEqual(which_one, 'a') def testInformedDecision(self): pk = 'test_informed_decision' decision = str(time.time()) # A is the clear winner, except when country=UK, in which case B wins opts = ['a', 'b', 'c', 'd'] self.whale.count_now([pk, decision, 'a'], None, dict(dollars=50000, visitors=10000)) self.whale.count_now([pk, decision, 'b'], None, dict(dollars=0, visitors=2000)) self.whale.count_now([pk, decision, 'b'], {'country': 'uk'}, dict(dollars=10000, visitors=2000)) self.whale.count_now([pk, decision, 'c'], None, dict(dollars=0, visitors=7500)) self.whale.count_now([pk, decision, 'd'], None, dict(dollars=5, visitors=1)) # Here's a visitor with no info -- 'A' should win by far. good, bad, test = self.whale.weighted_reasons(pk, decision, opts, formula='dollars/visitors') #_print_reasons(good, bad, test) self.assertEqual(True, 'a' in good.keys()) self.assertEqual(True, 'b' in bad.keys()) self.assertEqual(True, 'c' in bad.keys()) self.assertEqual(True, 'd' in test.keys()) # How about when we know the country is "UK"? good, bad, test = self.whale.weighted_reasons(pk, decision, opts, formula='dollars/visitors', known_data={'country': 'uk'}) #_print_reasons(good, bad, test) self.assertEqual(True, 'a' in good.keys()) self.assertEqual(True, 'b' in good.keys()) self.assertEqual(True, 'c' in bad.keys()) self.assertEqual(True, 'd' in test.keys()) chosen = {'a': 0, 'b': 0} for k in range(100): choose = self.whale.decide(pk, decision, opts, formula='dollars/visitors', known_data={'country': 'uk'}, bad_idea_threshold=0, test_idea_threshold=0) chosen[choose] += 1 self.assertEqual(True, chosen['b'] > 70, """A decision made 100 times between weights .15 vs .85 should have around 85 votes for 'b', we got %s, which is unlikely enough to fail a test, but not definitely indicative of a problem. If this test passes again on the next run, ignore the failure.""" % chosen) def testTrickyDecision(self): pk = 'test_tricky_decision' decision = str(time.time()) opts = ['en', 'sp', 'pt'] def count(geo, lang, dollars, visitors): self.whale.count_decided_now(pk, decision, lang, geo, {'dollars': dollars, 'visitors': visitors}) def justify(geo): #print #print 'Picking reasons for ', geo good, bad, test = self.whale.weighted_reasons(pk, decision, opts, 'dollars/visitors', geo) #print good.keys(), bad.keys(), test.keys() #_print_reasons(good, bad, test) return self.whale.decide(pk, decision, opts, 'dollars/visitors', geo, bad_idea_threshold=0, test_idea_threshold=0) k = 1000 m = k * k # Sure, these results seem predictable to a human # But what will our philosopher whale friend make of it? count('us', 'en', 1.5 * m, 300 * k) # $5/visitor, alright! count('us', 'sp', 1 * k, 10 * k) # $.10/visitor, well that is not surprising count('us', 'pt', 300, 5 * k) # $.06/visitor, :( count('mx', 'en', 100 * k, 100 * k) # $1/visitor, this almost works count('mx', 'sp', 200 * k, 100 * k) # $2/visitor aww yah! count('mx', 'pt', 200, 10 * k) # $.02/visitor lol count('br', 'en', 300 * k, 100 * k) # $3/visitor is good count('br', 'sp', 150 * k, 50 * k) # $3/visitor as well count('br', 'pt', 500 * k, 50 * k) # $10 JACKPOT self.assertEqual('en', justify('us')) self.assertEqual(True, justify('mx') in ['sp', 'en']) self.assertEqual('pt', justify('br')) def testWhaleCacheWrapper(self): t = str(time.time()) count = lambda: self.whale.count_now('test_cached', t) cached_sum = lambda clear=False: sum(self.whale.cached_plotpoints('test_cached', t, period='fivemin', unmemoize=clear)[t]['hits'].values()) # Set hits to 1 count() self.assertEqual(cached_sum(), 1) # Should stay 1 for a while for i in range(3): count() self.assertEqual(cached_sum(), 1) self.assertEqual(cached_sum(clear=True), 4)
def plotpoints(): whale = Whale() params = default_params() params['depth'] = g('depth', 0) params['period'] = g('period', '1x60') return json.dumps(whale.plotpoints(**params))
def graph(): from periods import Period params = { 'pk': g('pk', '_', False), 'dimension': g('dimension', '_', False), 'metric': g('metric', 'hits', False), 'depth': g('depth', 0), 'tzoffset': g('tzoffset', 0.0), 'period': g('period', str(Period.get(None))), 'area': g('area', ''), } pk = params['pk'] dimension = params['dimension'] metric = params['metric'] period = Period.get(params['period']) debug = g('debug', False) parent_div = g('parent_div', 'hailwhale_graphs') table = g('table', False) height = g('height', '300px') params['title'] = g('title', '') if not params['title']: pkname = g('pk', '') dimname = util.try_loads(g('dimension', 'Overall')) dimname = isinstance(dimname, list) and dimname[-1] or dimname params['title'] = '%s [%s]' % (util.maybe_dumps(pkname), util.maybe_dumps(dimname)) if isinstance(table, basestring): table = table.lower() == 'true' hwurl = req.GET.get('hwurl', req.url.split('graph.js')[0]) params['autoupdate'] = g('live', True) params['interval'] = g('interval', 6000) graph_id = hashlib.md5(str(params)).hexdigest() include_string = \ "document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/jquery.min.js'></script>\");"%hwurl if table: try: columns = int(g('table', 6, int)) except: columns = 6 pps = Whale.plotpoints(pk, dimension, metric, period=period, depth=params['depth']) dates = [p for p in Period.get(period).datetimes_strs() ][(-1 * columns - 1):] table_str = ''' $('#{id} .table').html('<table style="width: 100%"> <tr> <th></th> <th></th> {columns} </tr> '''.strip().format(id=graph_id, columns=' '.join([ '<th>%s</th>' % date.replace('00:00:00 ', '') for date in dates ])) dimensions = pps.keys() if '_' in dimensions: dimensions.remove('_') dimensions = ['_'] + dimensions for dimension_counter, dimension in enumerate(dimensions): checked = 'off' if dimension_counter < 10: checked = 'on' if dimension == '_': if params['depth']: continue dimension_name = '<b>Overall</b>' else: dimension_name = dimension.capitalize() table_str += ''' <tr> <td><input id="" style="display: none" type="checkbox" value="{checked}" name="checkbox-{pk}-{dimension}"></td> <td>{dimension_name}</td> {columns} </tr> '''.format(pk=pk, dimension=dimension, checked=checked, dimension_name=dimension_name, columns=' '.join([ "<td>%s</td>" % int(pps[dimension][metric][date]) for date in dates ])).strip() table_str += '''</table>');''' else: table_str = '' include_string = \ "document.write(\"<scr\" + \"ipt type='text/javascript' src='%sjs/hailwhale.min.js'></script>\");"%hwurl return_string = ''' appended=false;\n document.write('<div id="{id}"><div class="graph" style="height: {height}"></div><div class="table"></div></div>');\n function jqinit() {{\n if(typeof(jQuery) == 'undefined' || typeof(jQuery.hailwhale) == 'undefined') {{\n if(!appended) {{\n appended = true;\n {include_string}\n }}\n setTimeout(jqinit, 250);\n }} else {{\n $(function() {{\n $.hailwhale('{hwurl}').add_graph('{id} .graph', {options});\n {table_str} }});\n }} }} jqinit();\n '''.format(parent_div=parent_div, include_string=include_string, hwurl=hwurl, table_str=table_str, height=height, id=graph_id, options=util.maybe_dumps(params)) return return_string
class TestHailWhale(unittest.TestCase): def setUp(self): from hail import Hail from whale import Whale self.hail = Hail() self.whale = Whale() def testGetSubdimensions(self): t = 'subs_%s' % str(time.time()) self.whale.count_now(t, {'a': 1, 'b': 2}) subs = self.whale.get_subdimensions(t) assert('a' in subs) assert('b' in subs) def testGetAllSubdimensions(self): t = 'all_subs_%s' % str(time.time()) self.whale.count_now(t, {'a': 1, 'b': 2}) subs = self.whale.all_subdimensions(t) assert('a' in subs) assert(['a', '1'] in subs) assert('b' in subs) assert(['b', '2'] in subs) def testPlotpoints(self): t = str(time.time()) for i in range(5): self.whale.count_now('test_plotpoints', t, {'hits': 1, 'values': 5}) plotpoints = self.whale.plotpoints('test_plotpoints', t, ['hits', 'values'], points_type=list) self.assertEqual(plotpoints[t]['hits'][-1][1], 5) self.assertEqual(plotpoints[t]['values'][-1][1], 25) def testPlotpointsDepth(self): t = str(time.time()) self.whale.count_now('test_depth', {t: 'a'}) self.whale.count_now('test_depth', {t: 'b'}) self.whale.count_now('test_depth', {t: 'b'}) self.whale.count_now('test_depth', {t: {'c': 'child'}}) # Test 1 level deep plotpoints = self.whale.plotpoints('test_depth', t, points_type=list, depth=1) self.assertEqual(plotpoints[maybe_dumps([t, 'a'])]['hits'][-1][1], 1) self.assertEqual(plotpoints[maybe_dumps([t, 'b'])]['hits'][-1][1], 2) self.assertEqual(plotpoints[maybe_dumps([t, 'c'])]['hits'][-1][1], 1) self.assertEqual(False, maybe_dumps([t, 'c', 'child']) in plotpoints) # Test 2 levels deep plotpoints = self.whale.plotpoints('test_depth', t, points_type=list, depth=2) self.assertEqual(True, maybe_dumps([t, 'c', 'child']) in plotpoints) self.assertEqual(plotpoints[maybe_dumps([t, 'c', 'child'])]['hits'][-1][1], 1) # Test ranking and limiting i.e assign rank on the basis of value and then extract top limit candidate plotpoints = self.whale.plotpoints('test_depth', t, points_type=list,depth=1, limit=2) #self.assertEqual(plotpoints[maybe_dumps([t, 'b'])]['hits'][-1][1], 2) self.assertEqual(True, maybe_dumps([t, 'a']) not in plotpoints) self.assertEqual(True, maybe_dumps([t, 'c']) not in plotpoints) def testRatioPlotpoints(self): t = str(time.time()) for i in range(5): self.whale.count_now('test_ratio', t, {'hit': 1, 'value': 5}) plotpoints = self.whale.plotpoints('test_ratio', t, ['hit', 'value', 'value/hit'], points_type=list) self.assertEqual(plotpoints[t]['hit'][-1][1], 5) self.assertEqual(plotpoints[t]['value'][-1][1], 25) self.assertEqual(plotpoints[t]['value/hit'][-1][1], 5) def testRankSubdimensionsScalar(self): t = str(time.time()) self.whale.count_now('test_rank', [t, 'a', 'asub1'], {'value': 1}) self.whale.count_now('test_rank', [t, 'a', 'asub2'], {'value': 30}) self.whale.count_now('test_rank', [t, 'b'], {'value': 80}) self.whale.count_now('test_rank', [t, 'c'], {'value': 10}) ranked = self.whale.rank_subdimensions_scalar('test_rank', t, 'value') self.assertEqual(ranked[maybe_dumps([t, 'a'])]['important'], False) self.assertEqual(ranked[maybe_dumps([t, 'a', 'asub1'])]['important'], False) self.assertEqual(ranked[maybe_dumps([t, 'a', 'asub2'])]['important'], True) self.assertEqual(ranked[maybe_dumps([t, 'b'])]['important'], True) self.assertEqual(ranked[maybe_dumps([t, 'c'])]['important'], False) def testRankSubdimensionsRatio(self): t = str(time.time()) pk = 'test_ratio_rank' # OVERALL STATS: 529,994 value, 50,000 visitors, 10.6 value per visitor # Not important, too close to overall self.whale.count_now(pk, [t, 'a', 'asub1'], {'value': 54989, 'visitors': 4999}) # 11 value per visitor # Important, high relative ratio self.whale.count_now(pk, [t, 'a', 'asub2'], {'value': 375000, 'visitors': 25000}) # 15 value per visitor # Important, low relative ratio self.whale.count_now(pk, [t, 'b'], {'value': 100000, 'visitors': 20000}) # 5 value per visitor # Not important, not enough visitors self.whale.count_now(pk, [t, 'c'], {'value': 5, 'visitors': 1}) # 5 value per visitor one_level = self.whale.rank_subdimensions_ratio('test_rank_ratio', 'value', 'visitors', t, recursive=False) all_levels = self.whale.rank_subdimensions_ratio(pk, 'value', 'visitors', t) self.assertEqual(True, maybe_dumps([t, 'a', 'asub1']) not in one_level) self.assertEqual(all_levels[maybe_dumps([t, 'a', 'asub1'])]['important'], False) self.assertEqual(all_levels[maybe_dumps([t, 'a', 'asub2'])]['important'], True) self.assertEqual(all_levels[maybe_dumps([t, 'b'])]['important'], True) self.assertEqual(all_levels[maybe_dumps([t, 'c'])]['important'], False) def testBasicDecision(self): pk = 'test_basic_decision' decision = str(time.time()) # Make a decision, any decision, from no information whatsoever good, bad, test = self.whale.weighted_reasons(pk, 'random', [1,2,3]) #_print_reasons(good, bad, test) any_one = self.whale.decide_from_reasons(good, bad, test) self.assertEqual(True, any_one in [1, 2, 3]) # OK, now how about something somewhat informed? # This will be easy. Slogan A makes us huge profit. Products B and C suck. # D looks promissing but isn't yet significant opts = ['a', 'b', 'c', 'd'] self.whale.count_now([pk, decision, 'a'], None, dict(dollars=5000, visitors=1000)) self.whale.count_now([pk, decision, 'b'], None, dict(dollars=0, visitors=2000)) self.whale.count_now([pk, decision, 'c'], None, dict(dollars=0, visitors=2000)) self.whale.count_now([pk, decision, 'd'], None, dict(dollars=50, visitors=10)) good, bad, test = self.whale.weighted_reasons(pk, decision, opts, formula='dollars/visitors') #_print_reasons(good, bad, test) self.assertEqual(True, 'a' in good.keys()) self.assertEqual(True, 'b' in bad.keys()) self.assertEqual(True, 'c' in bad.keys()) self.assertEqual(True, 'd' in test.keys()) which_one = self.whale.decide(pk, decision, opts, formula='dollars/visitors', bad_idea_threshold=0, test_idea_threshold=0) self.assertEqual(which_one, 'a') def testInformedDecision(self): pk = 'test_informed_decision' decision = str(time.time()) # A is the clear winner, except when country=UK, in which case B wins opts = ['a', 'b', 'c', 'd'] self.whale.count_now([pk, decision, 'a'], None, dict(dollars=50000, visitors=10000)) self.whale.count_now([pk, decision, 'b'], None, dict(dollars=0, visitors=2000)) self.whale.count_now([pk, decision, 'b'], {'country': 'uk'}, dict(dollars=10000, visitors=2000)) self.whale.count_now([pk, decision, 'c'], None, dict(dollars=0, visitors=7500)) self.whale.count_now([pk, decision, 'd'], None, dict(dollars=5, visitors=1)) # Here's a visitor with no info -- 'A' should win by far. good, bad, test = self.whale.weighted_reasons(pk, decision, opts, formula='dollars/visitors') #_print_reasons(good, bad, test) self.assertEqual(True, 'a' in good.keys()) self.assertEqual(True, 'b' in bad.keys()) self.assertEqual(True, 'c' in bad.keys()) self.assertEqual(True, 'd' in test.keys()) # How about when we know the country is "UK"? good, bad, test = self.whale.weighted_reasons(pk, decision, opts, formula='dollars/visitors', known_data={'country': 'uk'}) #_print_reasons(good, bad, test) self.assertEqual(True, 'a' in good.keys()) self.assertEqual(True, 'b' in good.keys()) self.assertEqual(True, 'c' in bad.keys()) self.assertEqual(True, 'd' in test.keys()) chosen = {'a': 0, 'b': 0} for k in range(100): choose = self.whale.decide(pk, decision, opts, formula='dollars/visitors', known_data={'country': 'uk'}, bad_idea_threshold=0, test_idea_threshold=0) chosen[choose] += 1 self.assertEqual(True, chosen['b'] > 70, """A decision made 100 times between weights .15 vs .85 should have around 85 votes for 'b', we got %s, which is unlikely enough to fail a test, but not definitely indicative of a problem. If this test passes again on the next run, ignore the failure.""" % chosen) def testTrickyDecision(self): pk = 'test_tricky_decision' decision = str(time.time()) opts = ['en', 'sp', 'pt'] def count(geo, lang, dollars, visitors): self.whale.count_decided_now(pk, decision, lang, geo, {'dollars': dollars, 'visitors': visitors}) def justify(geo): #print #print 'Picking reasons for ', geo good, bad, test = self.whale.weighted_reasons(pk, decision, opts, 'dollars/visitors', geo) #print good.keys(), bad.keys(), test.keys() #_print_reasons(good, bad, test) return self.whale.decide(pk, decision, opts, 'dollars/visitors', geo, bad_idea_threshold=0, test_idea_threshold=0) k = 1000 m = k * k # Sure, these results seem predictable to a human # But what will our philosopher whale friend make of it? count('us', 'en', 1.5 * m, 300 * k) # $5/visitor, alright! count('us', 'sp', 1 * k, 10 * k) # $.10/visitor, well that is not surprising count('us', 'pt', 300, 5 * k) # $.06/visitor, :( count('mx', 'en', 100 * k, 100 * k) # $1/visitor, this almost works count('mx', 'sp', 200 * k, 100 * k) # $2/visitor aww yah! count('mx', 'pt', 200, 10 * k) # $.02/visitor lol count('br', 'en', 300 * k, 100 * k) # $3/visitor is good count('br', 'sp', 150 * k, 50 * k) # $3/visitor as well count('br', 'pt', 500 * k, 50 * k) # $10 JACKPOT self.assertEqual('en', justify('us')) self.assertEqual(True, justify('mx') in ['sp', 'en']) self.assertEqual('pt', justify('br')) def testWhaleCacheWrapper(self): t = str(time.time()) count = lambda: self.whale.count_now('test_cached', t) cached_sum = lambda clear=False: sum(self.whale.cached_plotpoints('test_cached', t, period='fivemin', unmemoize=clear)[t]['hits'].values()) # Set hits to 1 count() self.assertEqual(cached_sum(), 1) # Should stay 1 for a while for i in range(3): count() self.assertEqual(cached_sum(), 1) self.assertEqual(cached_sum(clear=True), 4)
def plotpoints(): whale = Whale() params = default_params() params["depth"] = g("depth", 0) params["period"] = g("period", None) return json.dumps(whale.plotpoints(**params))